Abstract
Peptide-based therapeutics have emerged as a significant treatment strategy, offering high specificity and tunable pharmacokinetics. Recent advances in Artificial Intelligence (AI) have shifted the focus towards structure prediction, generative design, and interaction modelling, significantly accelerating drug design and discovery. Deep learning architectures, including graph neural networks, transformers, and diffusion models, have facilitated the generation of novel sequences for the target of interest, although predicting the solubility, immunogenicity, and toxicity of these sequences remains a challenge. Innovations in peptide chemistry, such as cyclization, stapling, non-canonical amino acids, and nanoparticle formulations, help overcome the hurdles of bioavailability and permeation. These chemical approaches, combined with developments in autonomous peptide synthesis and high-throughput screening, have considerably reduced discovery timelines from years to months. Clinically, this progress is apparent in the growing number of approved peptide drugs for metabolic disorders, oncology, and medical imaging. Here, we review recent progress in peptide-based drug design using AI, focusing on generative architectures and interactions. We then examine AI-driven screening and delivery optimization for these peptide-based discoveries. Finally, we discuss the current limitations, practical challenges, and future direction with particular emphasis on data quality and autonomous drug discovery.
Introduction
Peptides have emerged as a compelling class of therapeutic interventions for various disorders.1 Since the initial use of insulin, the first peptide-based drug from the early 20th century, numerous modified peptides, such as cyclic and stapled forms, have been developed.2,3 These peptides exhibit enhanced potency on target interventions with unique features, including specificity, tunable half-life, and structural modifications.4 The approval for the peptide-based drugs has shown a consistent increase from 2020 to 2024 for various diseases, especially diabetes, obesity, and cancer.5 Peptides are also used as diagnostic markers in various disorders for imaging purposes, aiding clinical studies.6
Peptides are versatile molecules whose moderate size allows them to effectively target complex interactions, such as protein–protein interactions and binding to G protein-coupled receptors (GPCRs), with higher binding affinity.7,8 From the chemical perspective, the synthesizability and modifications, such as cyclization, non-canonical amino acids, stapling, and PEGylation, make it more suitable for therapeutic purposes.9-12 However, the drawbacks of the peptides are the poor oral bioavailability and vulnerability to proteolysis with limited tissue penetration.13,14 These factors urge increased dosage requirements and careful design for delivery and administration. Overcoming these negative factors is vital to progressing clinical drug development and discovery.
In recent years, science, technology, engineering, and medicine (STEM) fields have seen a transformative development in artificial intelligence (AI).15 For instance, protein folding, considered a significant challenge in protein studies, has been emulated with high accuracy using the Alphafold models.16 AI has been applied in various fields to unravel the hidden patterns and address the experimental difficulties, paving the way towards novel studies and discoveries.17,18 These foundations of AI have provided the capability for rational peptide design and docking studies. The AI-based models, such as ESM2,19 deep learning models,20 Generative Adversarial Networks (GANs),21 diffusion models,22 and Variational Autoencoders (VAEs),23 are applied to computationally design the peptide sequences for the specific target of interest.24 AI-based tools that predict key drug-like parameters such as toxicity, solubility, and permeability provide additional support for drug development by vastly reducing the experimental cost.25 Unifying these characteristic features using AI could aid in accelerating peptide-based drug discovery with greater success.
Numerous studies over the past five years report the integration of AI into peptide-based drug discovery. A recent review by Zheng et al. has listed progress in peptide discovery, synthesis, and clinical translation. At the same time, Xiao et al. provided a broad summary of peptide therapeutics, delivery platforms, and market trends, highlighting the evolving role of AI.2 Developments in peptide–protein interaction modeling, such as AfCycDesign for cyclic peptides, establish the effective use of computational methods to design cyclic peptides.26 These developments highlight a fundamental innovation in the field, where the power of experimental and computational methods converges to unravel novel therapeutic discoveries.
This review aims to provide a comprehensive and critical analysis of the current developments in AI-driven peptide-based therapeutics. We summarize the AI-based peptide prediction and modelling studies designed using the generative frameworks and their applications. We then delve into the AI-driven screening and delivery optimization for these peptide-based discoveries. We summarize the innovations of these peptide-based drugs from the clinical and translational perspective. Finally, we review the current limitations, practical challenges, and future direction with particular emphasis on the data quality and autonomous drug discovery.
Computational approaches in traditional peptide design
The traditional peptide drug discovery process has focused on empirical approaches such as screening large combinatorial libraries using phage display, bacterial display, and mammalian cell surface systems.27,28 These approaches express the peptide libraries on the protein or cell surfaces to identify the binding proteins through an iterative process. Phage display, in particular, has been widely utilized in peptide discovery that expresses the peptide variant as a genetic fusion to bacteriophage proteins, developing a direct link between the displayed peptide and its encoding DNA.29 The phase display libraries from the New England Biolabs have become a dominant tool that facilitates epitope mapping, protein–protein contact mapping, and identification of bioactive peptides. However, recent studies using next-generation sequencing showed a significant variation in the phage display libraries, including the bias in amino acid and over-representation of stop codons.30 Hence, advanced computational tools are needed to complement the traditional experimental methods (Fig. 1).
Fig. 1. Traditional approaches in peptide-based drug design.
This figure outlines the conventional computational workflow for identifying and optimizing peptide therapeutics. The process begins with homology modeling, which aids in predicting the three-dimensional structure of a peptide. Followed by screening of large peptide libraries for candidates with desirable properties. Quantitative structure–activity relationship (QSAR) modeling is applied to establish correlations between molecular features and the biological activity of the screened peptides. Molecular docking is further employed to predict the preferred binding orientation of a peptide with its target protein, providing insights into molecular recognition. Finally, molecular dynamics (MD) simulations capture atomistic details of peptide–protein interactions, stability, and conformational changes, offering critical guidance for lead optimization.
Structure-based drug design
Structure-based computational approaches have been central to peptide design, using protein structures to guide rational peptide optimization. Molecular docking appeared as a primary technique for modeling peptide–protein interactions, providing detailed information about binding modes and interactions.31 Traditional molecular docking approaches, however, encountered significant challenges when applied to peptides due to their intrinsic flexibility. Initial methods typically treated peptides as rigid entities, limiting their applicability to highly flexible peptide sequences.32 The development of flexible docking algorithms, such as CABS-dock,33 Hpepdock,34 and PatchMAN,35 represented a significant advancement by incorporating conformational flexibility of both peptide and protein molecules. For instance, CABS-dock utilizes a coarse-grained protein model that enables the search for peptide conformational space while retaining computational efficiency. Reports from various studies have used these tools in multiple systems, including peptide docking to G-protein-coupled receptors (GPCRs) and short linear motifs (SLiMs) in protein–protein interactions.36
Homology modeling and structure prediction
Homology modeling was widely used in peptide design when experimental structural data are unavailable. This computational method predicts three-dimensional peptide structures, assuming the folding pattern remains identical for peptides with similar sequences. However, the model accuracy relies mainly on the protein structure availability and the degree of sequence similarity between target and template peptides, with a minimum threshold of 30% sequence identity. Conventional tools such as I-TASSER have been valuable for discovering novel antimicrobial peptides and differentiating their anti-tumoral properties.37 While tools like Backbone-based Rotamer Library (BbRL) and PEP-FOLD have enabled prediction of peptide structures ranging from 5 to 50 amino acids, facilitating both de novo prediction and biased prediction when interaction sites are known.38
Virtual screening and library design
Virtual screening offers a cost-effective strategy to identify promising peptide candidates from vast computational libraries and has become a cornerstone of structure-based virtual screening (SBVS).39 SBVS utilizes protein structures to screen and design peptides with high affinity and specificity for relevant targets. Recent advances have aided in the development of ultra-large virtual peptide libraries containing billions of sequences available for screening. For instance, a study described a de novo design strategy using directed mutation-driven high-throughput virtual screening (HTVS) to develop vast virtual libraries, supposedly expanding from 104 initial scaffolds to 1014 library members through iterative mutation.40 The development of virtual libraries has also extended to include non-natural amino acids. Various studies have reported that approximately 380 000 readily synthesizable non-natural amino acids exist, demonstrating vast chemical diversity compared to the 20 natural amino acids. Virtual screening approaches using these expanded libraries have successfully identified peptides with significantly higher predicted affinities than natural sequences.41
Quantitative structure–activity relationship (QSAR) modeling
QSAR approaches are critical in drug discovery, bridging applications from small-molecule development to peptide optimization. However, they have had limited success compared to traditional pharmaceutical applications. QSAR studies in peptide research have explored various bioactive properties, including antioxidant activity, antimicrobial effects, and enzyme inhibition.42 A comprehensive review of QSAR applications to food protein-derived bioactive peptides revealed that while some studies successfully identified structural requirements for specific bioactivities, differences across analyses were common, possibly due to dataset quality and descriptor selection issues.43 The limited availability of high-quality experimental data for peptide–protein interactions has constrained the development of robust QSAR models. Recent advances have introduced machine learning-guided QSAR approaches tailored explicitly for peptides. The streaMLine program represents a noteworthy example, systematically exploring peptide libraries through rigorous design, synthesis, screening, and ML-driven analysis. Researchers successfully screened 2688 peptides using this approach and identified multiple options for designing stable and potent GLP-1R agonists.44
Molecular dynamics (MD) simulations
Molecular dynamics (MD) simulations play a pivotal role in computational studies for peptide-based drug design, providing an in-depth understanding of peptide–protein interactions and dynamics at the atomistic level, which is essential for drug design.45-47 Numerous studies have reported the use of MD simulations to rationally design the inhibitory peptides against various disorders.48-55 Advanced MD simulation techniques, such as enhanced sampling and replica-exchange, have further enabled the study of cyclic and intrinsically disordered peptides.56-58 By elucidating binding mechanisms through broad views of molecular interactions, MD simulations can identify probable binding poses, calculate binding affinities, and determine the thermodynamic and kinetic parameters governing peptide–target interactions.59-61 MD simulations have also been utilized significantly in anti-microbial peptide design, revealing membrane disruption mechanisms and physicochemical properties that enhance activity against resistant bacterial strains.62-64 Our group has developed discrete molecular dynamics (DMD), which employs discrete energetic potentials to reach microsecond-scale timescales for complex biomolecular systems efficiently.65,66 DMD has been extensively validated and applied to explore protein folding dynamics, aggregation mechanisms, and prion-like conformational conversions.67-69 It has also provided insights into the molecular mechanisms of polypeptide aggregation in human diseases70 and the effects of macromolecular crowding on folding cooperativity.71 More recently, DMD has been successfully applied to rational peptide design, including the development of therapeutic peptides against various disorders.72-75 Together, these studies demonstrate the versatility of DMD in uncovering atomistic mechanisms of peptide aggregation, protein–peptide interactions, and structure-based peptide design. With the current advancements in computational resources that support MD simulations of large systems over millisecond timescales, they accelerate the advantage in peptide drug discovery.
Accurate prediction of the peptide target interactions and binding affinity via MD simulations is dependent on the choice of force field and selection. The AMBER force field (ff14SB, ff19SB) is widely used for protein–peptide complex systems, capturing the conformational dynamics essential for binding.76 A recent study by Miao et al. suggested that among seven force fields tested for cyclic peptide simulations, RSFF2 + TIP3P, RSFF2C + TIP3P, and Amber14SB + TIP3P showed the highest accuracy, recapitulating NMR-derived structures for 10 peptides, while others performed significantly worse.77 CHARMM36m and Amber ff99SB-disp force fields are shown to provide better efficiency in capturing the range of dynamics from intrinsically disordered peptides, which can accurately capture experimental calculations.78-80 Furthermore, the MM-PBSA and MM-GBSA methods provide estimates of binding affinities by computing the enthalpic contribution from force field energies and the entropic penalty from conformational sampling.81
Despite significant advances, traditional computational approaches face several persistent challenges. The inherent flexibility of peptides makes accurate structure prediction and docking computationally demanding. Limited availability of experimental structural data for peptide–protein complexes constrains model training and validation.82 Additionally, traditional methods often struggle with non-canonical amino acids and chemical modifications commonly used in therapeutic peptides. Incorporating cyclization, stapling, and other structural modifications remains a significant challenge for conventional modeling approaches.
AI-enabled structure prediction for peptide-based design
Over the past decade, AI has revolutionized the field of peptide-based drug discovery. From the Nobel Prize-winning development of AlphaFold to the upcoming quantum-based generative models, AI has enhanced research approaches towards peptide structure prediction, interaction, and therapeutic design (Fig. 2).
Fig. 2. Artificial Intelligence in peptide structure prediction.
The prediction of peptide structures has shown a transformative shift with the integration of artificial intelligence (AI) and advanced computational methods. Deep learning–based models such as AlphaFold, RoseTTAFold, and ESMFold are trained on vast datasets of protein sequences and experimentally determined structures. As a result, they predict the 3D structure of novel peptide sequences with exceptional accuracy. This innovation has redefined peptide modeling, opening new avenues for drug discovery, protein engineering, and therapeutic design.
AlphaFold for structure prediction
AlphaFold, introduced by DeepMind, made a breakthrough in computational structural biology.16 The AlphaFold, released in early 2020, demonstrated astonishing accuracy in the protein structure prediction competition CASP, with a median backbone accuracy of 0.96 Å RMSD, compared to 2.8 Å for competing methods. This development showed immediate applications towards peptide research as the AlphaFold database now holds 200 million protein structures with vast data information. The recent development of AlphaFold3 has displayed the capability of predicting protein–peptide interactions.83 Independent validation studies on the 588 peptide sequences with varying amino acid ranges (10–40) showed that AlphaFold could predict the peptides with better accuracy.84 However, the flexible regions and intrinsically disordered peptides still show reduced reliability.
Complementary structure prediction platforms
RoseTTAFold is an alternative to AlphaFold, with a neural network architecture that simultaneously considers sequence patterns, amino acid interactions, and three-dimensional structure.85 RoseTTAFold revealed precise value in modeling protein complexes and extended to handle nucleic acid–protein interactions through RoseTTAFoldNA.86 The RoseTTAFold all-atom (AA) model can accept a wide range of ligands and covalently modified amino acids, which is particularly significant for peptide design, as it enables modeling of non-natural amino acids and chemical modifications commonly used in therapeutic peptides. ColabFold has made high-quality protein structure prediction accessible to the broader research community by combining AlphaFold 2 with accelerated homology search algorithms. The platform achieves 40–60 fold faster sequence searching than standard AlphaFold 2 pipelines while maintaining prediction accuracy.87 ColabFold supports both homo and heteromeric complex prediction, with specific handling of peptide–protein interactions. ESMFold, an embeddingbased predictor leveraging protein language models, has exhibited applicability for peptide sequences, with significant hits capturing short-range interactions relevant to secondary structure formation.19
Beyond these predictors, particular AI frameworks dedicated to peptide modeling are also emerging. For instance, PEP-FOLD3 employs a de novo fragment assembly strategy integrated with neural network-based scoring functions to capture low-energy peptide conformations.38 The refinement of PepFold’s pipeline has extended its use for linear and cyclic peptides, including post-translational modifications. These methods employ reinforcement learning strategies to iteratively refine peptide conformations against experimentally derived constraints such as NMR or cryo-EM data.
Recently, the AfCycDesign framework adapted AlphaFold2 with cyclic positional encoding, enabling accurate structure prediction and sequence redesign of cyclic peptides.26 Notably, several de novo designs yielded experimental confirmation with RMSDs under 1 Å, and served as effective scaffolds for nanomolar peptide binders against targets like MDM2. These results underscore the growing capability of AI models to generate and validate stable peptide architectures, with significant implications for macrocyclic therapeutic development.
Deep learning approaches for peptide–protein interaction prediction
Deep learning has transformed peptide–protein interaction (PPI) prediction by aiding to that capture complex binding patterns. Modern neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, have shown better performance over traditional machine learning approaches. These sophisticated models can simultaneously predict binary peptide–protein interactions and identify critical binding residues, providing multi-level insights that accelerate peptide drug discovery processes (Fig. 3).
Fig. 3. Deep learning architectures for predicting protein–peptide interactions.
The prediction of protein–peptide interactions, a key step in therapeutic discovery, has been significantly advanced by applying diverse deep learning architectures. Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs) have been widely used to extract features from sequence data, while Graph neural networks (GNNs) capture the complex three-dimensional topology of molecular structures. Generative models, such as variational autoencoders (VAEs), enable the design of novel peptides with tailored properties. More recently, transformers and protein language models (PLMs) have provided exceptional insights into molecular context, achieving state-of-the-art predictive performance.
Convolutional neural networks (CNNs) for sequence-based prediction
Convolutional neural networks are the foundational architectures for peptide–protein interaction prediction, particularly effective at capturing local sequence patterns and motifs for binding interactions.88 PepCNN represents a significant advancement, incorporating structural and sequence-based information from primary protein sequences to predict peptide binding residues.89 Recent developments have extended CNN applications to multi-level peptide–protein interaction prediction. The CAMP (comprehensive analysis of multi-level peptide–protein interactions) framework employs CNN modules to extract hidden contextual features from both peptides and proteins.90 The framework incorporates multi-channel architectures to separately process numerical and categorical features, addressing inconsistencies that arise from multi-source feature integration. This design enables simultaneous binary interaction prediction and peptide-binding residue identification, providing comprehensive insights into interaction mechanisms. Thus, CNNs efficiently capture local sequence motifs through hierarchical feature extraction, enabling fast inference for high-throughput peptide screening with minimal preprocessing requirements. However, their limited capacity to model long-range dependencies and inherent lack of 3D structural awareness constrain accurate prediction of binding interactions and conformational properties.
Transformer architectures and attention mechanisms
Transformer-based models have revolutionized peptide–protein interaction prediction by using attention mechanisms to capture long-range dependencies and identify critical interaction patterns. The cross-TCR-interpreter model utilizes transformer architectures in predicting T-cell receptor-peptide-major histocompatibility complex (TCR-pMHC) interactions. ABTrans represents another innovative transformer application, specifically designed for predicting interactions between amyloid-b peptides and antibodies.91 PepNN advances transformer applications by enabling sequence- and structure-based predictions through graph attention layers.92 The model incorporates multi-head reciprocal attention layers that simultaneously update embeddings of both peptides and proteins, learning interactions between residues involved in binding. More recently, TPepPro,93 a transformer-based model trained on 19 187 peptide–protein complex pairs, achieved 85.5% accuracy with an AUC of 0.922. The model combines local protein sequence feature extraction with global protein structure feature extraction, demonstrating the power of attention mechanisms in capturing long-range interactions. Transformer models leverage self-attention mechanisms to capture long-range sequence dependencies and global context, achieving state-of-the-art performance across diverse peptide prediction tasks with highly parallelizable training. Nonetheless, their quadratic computational complexity with respect to sequence length, substantial memory requirements, and dependence on large training datasets limit their practical deployment for resource-constrained applications and small peptide datasets prone to overfitting.
Graph neural networks for structural interaction modeling
Graph neural networks model protein–protein interfaces and peptide binding sites by representing molecular structures as graphs with nodes representing residues or atoms and edges, capturing spatial relationships.94 The TP-LMMSG model significantly advances therapeutic peptide prediction, incorporating hierarchical multi-scale residual networks with pre-trained language model embeddings.95 This approach achieves remarkable performance across antimicrobial peptide (AMP), antiviral peptide (AVP), and anticancer peptide (ACP) prediction tasks while reducing preprocessing time by over seven-fold compared to traditional graph learning models. Research utilizing graph convolutional networks (GCN) and graph attention networks (GAT) shows that structural information combined with sequence features considerably enhances prediction performance.96 These models construct protein graphs from protein files within threshold distances, creating residue contact networks that capture spatial relationships critical for interaction prediction.
Integrating protein language models with graph neural networks represents a substantial improvement. By using pre-trained language models like SeqVec and ProtBERT to generate node features directly from protein sequences, these hybrid approaches eliminate the need for domain-specific features while maintaining the structural knowledge provided by graph representations.97 This combination leverages evolutionary information learned by language models and spatial constraints captured by graph architectures. Hence, graph neural networks naturally represent molecular topology and 3D spatial relationships, enabling explicit modeling of atom-level interactions critical for accurate binding affinity and protein–peptide complex prediction. Their computational expense for large molecular systems, strict requirement for high-quality structural inputs, and limited generalization to novel structural motifs outside training distributions present significant practical limitations.
Recurrent neural networks and LSTM applications
Long short-term memory (LSTM) networks are used for peptide property prediction and sequence generation since the network captures long-range interactions in amino acid sequences.98 The use of LSTM networks to generate antimicrobial peptides exhibits their generative capabilities.99 Recent implementations have combined LSTM networks with other architectural components to improve performance. Models combining convolutional layers with bidirectional LSTM capture local patterns and long-range dependencies simultaneously. This hybrid approach is remarkably effective for T-cell receptor–epitope binding prediction, where local sequence motifs and global sequence context contribute to binding specificity.100 However, slow sequential training that precludes parallelization, vanishing gradient problems affecting long sequences, and inferior performance compared to Transformer architectures on contemporary benchmarks have diminished their adoption in modern peptide design workflows.
Protein language models and BERT-based approaches
Pre-trained protein language models have transformed peptide–protein interaction prediction by providing contextualized representations of amino acid sequences. BERT-based models, such as PepBCL, reveal substantial advantages over traditional hand-crafted features. This approach is particularly effective for peptide-binding residue prediction, where only approximately 5.4% of residues interact.101 PeptideBERT represents a specialized application of transformer language models to peptide property prediction.102 The model’s success demonstrates that language models can effectively capture the relationship between sequence and function for therapeutic peptide development. The SWING (sliding window interaction grammar) framework extends protein language model applications with contrastive learning for protein–peptide interaction.103 Thus, protein language models pre-trained on vast sequence databases capture evolutionary and functional patterns, enabling powerful transfer learning with strong zero-shot and few-shot prediction capabilities that substantially reduce experimental data requirements. However, their sequence-only representations fundamentally ignore structural dynamics, require computationally intensive fine-tuning, and may inadequately represent rare peptide classes underrepresented in natural protein training data.
Variational autoencoders for peptide generation and optimization
Variational autoencoders are used in peptide design and generation by learning continuous latent representations of sequence space. The VAE-MH (variational autoencoder with Metropolis–Hastings) represents the first deep learning-based model explicitly designed for peptide extension. This approach learns from protein–protein interactions and conducts focused searches for optimal sequences rather than random exploration.104
Applications to antimicrobial peptide generation showcase VAE effectiveness in functional peptide design. VAE models trained on known antimicrobial peptides can generate novel sequences retaining critical features such as high hydrophobic moment in alpha-helical peptides while exploring sequence variations.105 Recent developments have widened VAE applications to protein conformational exploration. This approach reveals VAE utility beyond sequence generation, extending to structural modeling and conformational analysis for understanding peptide–protein interactions. Hence, VAEs offer continuous latent spaces, enabling smooth interpolation and property-guided optimization, within probabilistic frameworks that quantify uncertainty and facilitate efficient training on moderately sized datasets. Posterior collapse frequently reduces latent space informativeness, generated sequences often exhibit limited diversity, and fine-grained control over specific structural features remains challenging.
Generative AI for peptide design
Generative AI has shown a transformative approach in peptide-based drug discovery, presenting the ability to design novel peptide sequences with therapeutic properties. Unlike traditional methods, which rely heavily on exhaustive screening, generative models learn the underlying patterns of protein sequences and structures from vast biological datasets. By leveraging architectures such as variational autoencoders (VAE), generative adversarial networks (GAN), diffusion models, and protein language models (PLM), these systems can design peptides that are not only diverse but also optimized for stability, specificity, and efficacy (Fig. 4). This paradigm accelerates discovery while reducing cost and experimental workload.
Fig. 4. Advanced generative AI tools for peptide discovery.
This schematic illustrates the diverse AI models that generate and refine novel peptides with therapeutic potential. Variational autoencoders (VAE) and convolutional neural networks (CNN) enable the generation of diverse peptide candidates, while graph adversarial Networks (GAN) incorporate structural context into design. Reinforcement learning (RL) is applied for goal-directed optimization, guiding peptides toward desired properties. More advanced approaches, including Protein language models (PLM) and diffusion models (Diff), are expanding the field further by learning the fundamental rules of protein structure and function, enabling the creation of highly specific and compelling peptide therapeutics.
Variational autoencoders and generative adversarial networks
The application of generative AI to peptide design has progressed considerably over the past decade. VAEs are one such model used for antimicrobial peptide discovery, with frameworks like PepVAE representing semi-supervised learning approaches that jointly model labeled and unlabeled peptide sequences.106 These studies have shown that VAE-generated peptides can be experimentally validated, with some novel antimicrobial peptides discovered within 48 days of computational prediction. The HydrAMP system advances VAE applications through conditional variational autoencoders (cVAE) that unravel antimicrobial properties from other latent features.107 This approach leverages parameter-controlled creativity to generate diverse peptides for multiple tasks, including unrestrained generation and analogue design.
Generative adversarial networks (GANs) have applications in peptide design. PandoraGAN specifically targets bioactive antiviral peptides using modified GAN architectures adapted from natural language processing.108 AMPGAN v2 represents a significant advancement, employing bidirectional conditional GANs (BiCGAN) for rational AMP design.109 HelixGAN addresses structural peptide design by generating de novo left-handed and right-handed alpha-helix structures at the atomic level.110 The model employs gradient-based latent space optimization to match exact conformations of selected hotspot residues. However, GANs face challenges, including mode collapse and training instability, limiting their broader adoption.
Diffusion models for backbone and interface generation
Diffusion-based models have become noteworthy in the development of peptide de novo and interface design in recent years. Remarkably, RFdiffusion showed that denoising the generative trajectories can aid in forming the backbone to fit the target and yield high-affinity binders.111 That was comprehensive to helical peptides using partial diffusion refinement of the ligands, yielding picomolar binding validated experimentally.112 Meanwhile, RFpeptides adapts the diffusion framework to macrocyclic peptides to generate cyclic backbones that match the protein pockets, aiding stability and affinity.113 Similarly, all-atom peptide generation with the geometric latent diffusion (PepGLAD) conditioning on the binding site geometry to produce sequences and 3D structures, benchmarking the test sets.114 Further, ProteinMPNN, a message-passing neural network for sequence design from structure, can redesign a scaffold to bind a peptide epitope, significantly improving affinity in experimental assays when paired with Rosetta-based assembly.115
DiffPepBuilder, an SE(3)-equivariant diffusion model trained on a synthetic dataset (PepPC-F), enables de novo design of peptide binders co-optimizing sequence and conformation, presenting stabilizing disulfide bonds.116 In comparative benchmarks, DiffPepBuilder exceeded AfDesign and RFdiffusion with ProteinMPNN scaffolding in producing diverse, structurally faithful binding peptides with improved free energy profiles. Besides, HYDRA represents another innovative diffusion-based approach, combining target-aware amino acid residue generation with binding affinity maximization.117 This hybrid model generates high-quality, diverse, and stable peptide binders tailored to target receptor proteins.
PepTune, introduced in 2025, uses a masked diffusion language model guided by Monte Carlo Tree Guidance (MCTG) to optimize for binding affinity, solubility, permeability, hemolysis, and non-fouling characteristics simultaneously.118 This modular, search-guided framework represents a powerful step forward in optimizing multiple biophysical properties in one model. Specialized application of antimicrobial peptides has yielded compelling results. AMPGen integrates an autoregressive diffusion generator with evolutionary information from MSAs, a discriminative XGBoost screen for physicochemical features, and an LSTM-based scorer for target specificity.119 Experimental validation showed that of 40 de novo peptides synthesized, over 80 percent displayed antibacterial activity, and these sequences were absent from existing AMP databases, demonstrating novelty and efficacy. Beyond AMPs, generalpurpose diffusion frameworks are emerging. CPL-Diff, a mask-controlled diffusion model, allows length tuning during peptide generation, offering flexibility in sequence design that accommodates therapeutic constraints like half-life or membrane binding.120 MMCD (multimodal contrastive diffusion) integrates both sequence and structure information in a contrastive diffusion learning framework, boosting generation quality across antimicrobial, anticancer, diversity, and docking metrics.121 Besides, their computational expense requiring 200–1000 denoising steps, slow inference limiting throughput, dependence on high-quality structural training data, and practical deployment challenges without GPU infrastructure constrain widespread accessibility.
Flow matching models for peptide design
Flow matching models represent a new class of continuous generative architecture that learn to map the prior distribution into complex distributions of biologically relevant structures. Unlike diffusion models that rely on static denoising, flow matching uses deterministic ordinary differential equations to transfer data points from latent to molecular space, achieving high efficiency.122,123 This framework supports flexible generations, allowing for the rapid sampling of diverse structural motifs, such as alpha helices, beta sheets, and cyclic peptides. Flow matching enables large-scale screening of candidate peptides within seconds and could incorporate constraints such as secondary structure propensity.
ProtFlow124 leverages compressed protein language model embeddings to efficiently generate peptides and antibodies in a single inference step, while HelixFlow125 extends this capability to full-atom, SE(3)-equivariant helical peptide design, including pocket-specific inpainting for receptor-guided binder generation. Recently developed NCFlow is able to incorporate arbitrary uses of the non-canonical amino acid in protein design.126 However, these lack induced fit modeling, thus requiring further refinement with docking or MD simulations to ensure the validity. Despite these limitations, flow matching offers a powerful tool for early-stage screening and the rapid generation of peptide libraries.
Comparatively, diffusion models are more preferable when structural fidelity and experimental validation are prioritized, while flow-matching methods are ideally suited for rapid, large-scale exploration and early-stage design under limited computational resources. In the near future, the use of hybrid architectures with diffusion and flow matching is likely to determine the next generation of peptides, as supported by equivariant modeling, attention mechanisms, and guided optimizations.
BindCraft AlphaFold2-based one-shot functional design
BindCraft represents a distinct paradigm in peptide design, diverging from the diffusion and flow-based models by leveraging AlphaFold2 to optimize the peptide-target complex through iterative confidence maximization directly.127 BindCraft co-folds both the target and binder, enabling induced-fit modeling that captures the conformational adaptation upon binding. Initially, BindCraft was tested on miniprotein (60–200 amino acids) binders, but recent studies have demonstrated its performance on short peptides, achieving nanomolar binding (65–650 nM) against MDM2 and WDR5.128 The streamlined multimer protocol allows users to define peptide length, binding hotspots, and filtering stringency, optimizing efficiency for short peptides that primarily function as binding interfaces. Its main limitation lies in structural diversity, as current implementations favor alpha-helical motifs, restricting applicability to targets with helical recognition interfaces. Despite its computational intensity, BindCraft’s interpretability and capacity for target-specific sequence refinement make it a top tool for precision-driven binder design.
Protein language models for target-conditioned peptide generation
Protein language models (PLMs) enable the generation of target-conditioned peptides without explicit structural templates. PepMLM fine-tunes ESM-2 with a masking strategy that conditions on the target protein sequence, reconstructing the cognate peptide region and yielding binders validated both in silico and experimentally, including degradation assays.129 Recent reports show peptides designed directly from protein sequences, underscoring that sequence-only conditioning can discover functional binders at scale when paired with robust structural inspection.130 These PLM-centric generators are attractive for proteome-wide scans, and they interface naturally with downstream structure models to eliminate false positives and select pose-consistent candidates for synthesis. Similarly, AMP-designer, an LLM-based model, emphasizes speed and potency.131 Within 48 days, it produced 18 AMP candidates, of which nearly all were active in vitro. Two exhibited high efficacy, low hemotoxicity, robust plasma stability, and strong in vivo performance, suppressing bacterial load by ~100-fold in murine lung infection models. This performance highlights the feasibility of rapid, AI-driven design-to-validation loops even in low-data or strain-specific contexts. Therefore, target-conditioned protein language models integrate target protein information to generate binder-specific sequences, leveraging evolutionary co-variation patterns for rational, context-aware peptide design that accounts for binding partner characteristics. The scarcity of paired peptide-target training data, suboptimal conditioning mechanisms, and inheritance of biases from pre-training on natural protein sequences limit their current effectiveness for de novo therapeutic peptide discovery.
Backbone-to-sequence decoding with (ligand) MPNN
Generative workflows commonly split shape from sequence: diffusion proposes peptide/receptor geometries, and a decoder assigns sequences that stabilize the interface. ProteinMPNN remains a standard for fast, high-quality sequence design on fixed backbones, with strong experimental validation across diverse scaffolds.115 LigandMPNN extends this concept for chemically rich environments by conditioning sequence design on atomic context, including small molecules, metals, and nucleotides, thereby supporting peptides and receptors that bind cofactors or include bound ligands in the pocket.132 In peptide design, the pair forms a practical core to propose peptide conformations against a target surface, decode sequences with (Ligand)MPNN to optimize packing and polar satisfaction, and then re-score with AlphaFold-multimer to check pose stability and side-chain realism before experimental selection.
Reinforcement learning and search over constrained chemistries
Reinforcement learning (RL) improves generative models by imposing design constraints, including cyclization, incorporating non-canonical residues, and integrating cell-penetrating motifs. Recent RL frameworks assemble and score peptides in closed chemical spaces, for example, CYC_BUILDER for target-specific cyclic peptides using Monte Carlo tree search (MCTS) and bond-forming actions to optimize binding while ensuring synthetically viable cyclization.133 Broader RL approaches, including graph-attention and prior-guided RL, efficiently explore peptide sequence/structure space, balancing novelty and physicochemical properties.134 Diffusion or PLM proposals can act as priors, while rewards integrate docking scores and heuristic developability metrics. This RL layer is especially valuable for macrocycles and stapled designs, where combinatorial chemistry explodes and naive generators drift from synthesizable, bioactive regions of sequence space. Consequently, RL frameworks enable simultaneous multi-objective optimization balancing efficacy, safety, and drug-likeness, and incorporating iterative experimental feedback for continuous model improvement. Careful reward function design proves challenging and susceptible to sample-inefficient training demands extensive computational simulations, and training instability, particularly with sparse rewards, impedes reliable convergence.
Non-canonical residues and macrocyclic design
Generative models increasingly target non-natural amino acids and macrocycles to address protease stability and permeability. PepINVENT reports a sequence generator explicitly beyond natural amino acids, enabling exploration of peptidomimetic design spaces relevant to oral bioavailability and serum stability.135 Diffusion pipelines specialized for macrocycles generate cyclic backbones shaped to receptor pockets, a crucial advance because ring closure and side-chain geometry strongly constrain feasible sequences.113 On the validation side, AlphaFold3 (AF3) allows complex modeling with modified residues and ligands, which helps vet non-canonical designs and flag steric or coordination issues before synthesis. Modeling of cyclic peptides with AF3 is emerging, indicating improved fidelity for unnatural chemistries and providing a standardized route for pose confirmation across modified peptide classes.136 Thus, incorporation of non-canonical amino acids and macrocyclic constraints dramatically expands chemical diversity beyond the 20 natural amino acids, enhancing protease resistance, metabolic stability, and enabling novel binding modes inaccessible to linear peptides. Limited training data for non-standard modifications, increased synthetic complexity and cost, unpredictable pharmacokinetic profiles, and less-established regulatory approval pathways present substantial barriers to clinical translation.
Programmable generative models and conditioning (Chroma & beyond)
Programmable generators such as Chroma sample structures and sequences under explicit constraints, symmetry, topology, or shape, can then be conditioned toward epitope presentation or pocket complementarity, offering scaffolds that support short peptide motifs or present peptide-like surfaces for binding.137 In peptide design, these scaffolds serve as templates: a peptide motif is embedded or recognized by the generated protein surface, after which MPNN-style decoding and AF-based validation refine the complex. Together with PLM-conditioned peptide generators (PepMLM) and diffusion-based macrocycle design, such programmable models create an end-to-end toolkit.
Despite remarkable progress, several challenges persist in AI-enabled peptide design. Current models struggle with highly flexible regions and intrinsically disordered peptides. The limited availability of high-quality experimental data for peptide–protein complexes also constrains model training and validation. Optimization remains computationally expensive, and navigating these limits for multiple properties remains a challenge. There are also concerns about novelty versus acceptability as systems like AMPGen show promise, but whether such peptides scale in manufacturability and clinical safety remains unknown. Future designs are inclining toward foundation models, self-driving lab, and richer structural conditioning to address these.
Integration of rational design with generative AI, genetic algorithms, simulations, and experiments
Current peptide drug discovery approaches employ hybrid strategies that combine rational design with generative AI and physics-based simulations. This integrated method enhances the strategies by providing a deeper mechanistic understanding through rational design. Meanwhile, AI enables rapid predictions across vast, complex spaces with atomistic calculations from MD simulations, offering deeper insights to advance experimental studies.138,139
Chang et al. employed a latent diffusion model (AMPdiffusion), fine-tuned on antimicrobial peptide sequences using protein language model embeddings. From 50 000 generated candidates, 46 top peptides were synthesized and experimentally validated, demonstrating broad-spectrum antibacterial activity against various pathogens.140 Chen et al. developed deep learning models combining support vector machines with AlphaFold2 structure predictions to design cysteine- and lysine-stapled peptides. Experimentally validated stapled peptides showed broad-spectrum antimicrobial activity (MIC 2–8 μg mL−1), excellent serum stability, and minimal hemolytic activity, successfully integrating computational prediction with rational stapling modifications.141 Wang et al. employed a large language model-based foundation model generating candidates from extensive peptide sequence databases. Of the 18 synthesized peptides, 17 exhibited antimicrobial activity against ESKAPE pathogens, with five AMPs achieving MICs of 4–16 μg mL−1 against multidrug-resistant Gramnegative bacteria and demonstrating a reduction of ~99% in bacterial load in mouse pneumonia models.142 Ortega et al. combined RNN-LSTM and GAN architectures with helical wheel analysis to generate 6004 peptide candidates. From 12 synthesized peptides, 9 achieved MIC values below 10 μM against various pathogens, with OrP1M, OrP9M, and VeP1 demonstrating MIC as low as 2 μM, and six peptides showing anticancer activity against MCF-7 cells.143
Combined VAE with Metropolis–Hastings sampling to generate peptide extensions for β-catenin inhibitors, using MD simulations for binding pose refinement and MM/GBSA affinity calculations. Experimentally validated peptides achieved IC50 values of 0.03 μM (β-catenin), demonstrating the effectiveness of iterative VAE fine-tuning with MD-guided selection.144 Zhao et al. developed a conditional denoising VAE framework that integrates a transformer architecture with guidance on physicochemical properties for the generation of AMPs. The model incorporated denoising techniques to address data sparsity and enhance generalization, enabling the generation of AMPs with preserved desirable properties and reduced hemolytic activity while maintaining antimicrobial efficacy.145 Wang et al. employed a transformer-based VAE for the latent diffusion model, integrating MD simulations to validate membranebinding stability and antibacterial mechanisms.146 Das et al. utilized a VAE trained on 1.7 million peptide sequences combined with MD simulations in explicit membrane environments to analyze conformational stability and antimicrobial mechanisms. Generated and experimentally validated two novel AMPs within 48 days, demonstrating rapid processing in the drug discovery process while maintaining potent activity and low toxicity147
The most successful peptide discovery programs implement active learning cycles where experimental data continuously refines computational models. After initial computational design and synthesis, experimental assays (binding affinities, antimicrobial activity, cytotoxicity, pharmacokinetics) provide ground-truth data. These results retrain predictive models, update reward functions in reinforcement learning frameworks, and refine physics-based force field parameters, progressively improving prediction accuracy and reducing experimental attrition rates.
Therapeutic screening and translational optimization
Transforming peptides from laboratory discoveries into viable therapeutics requires addressing critical challenges, including toxicity, immunogenicity, solubility, and delivery. AI has emerged as a sophisticated tool across each domain, offering unparalleled insights into peptide optimization (Fig. 5).
Fig. 5. AI-driven therapeutic optimization in peptide drug design.
This figure illustrates the use of artificial intelligence to streamline the optimization of novel peptide therapeutics. Predictive models enable the rapid screening of candidates across key properties, including toxicity, solubility, membrane permeability, and immunogenicity. By integrating these assessments into a computational design framework, AI facilitates efficient multi-parameter optimization, guiding the selection of peptides with improved efficacy and an increased likelihood of clinical success.
Toxicity and safety assessment
Modern toxicity prediction has evolved from simple sequence-based approaches to sophisticated multimodal architectures. ToxGIN represents a significant advancement, utilizing graph isomorphism networks (GIN) to integrate structural information with sequence data for peptide toxicity prediction.148 The model performs better (F1 score = 0.83, AUROC = 0.91) by representing peptide structures as graphs where amino acids serve as nodes and spatial interactions as edges, incorporating ESM-2 embeddings and physicochemical properties. Similarly, tAMPer demonstrates the power of structure-aware prediction by combining ESM-2 protein language model embeddings with ColabFold-predicted structures.149 The multimodal framework extracts structural features using graph neural networks while capturing sequential dependencies through recurrent networks, achieving 91.7% AUROC and establishing strong correlations with experimental HC50 values.
Physicochemical properties prediction
Immunogenicity assessment has advanced significantly through transformer and attention-based architectures. UnifyImmun represents a unified framework that simultaneously predicts peptide binding to HLA and TCR molecules using cross-attention mechanisms.150 The model employs virtual adversarial training to enhance generalizability, achieving superior performance on multiple test sets and outperforming state-of-the-art methods by over 10% on large-scale COVID-19 datasets. Besides, peptide solubility prediction has benefited from advanced neural architectures addressing oral and parenteral delivery requirements. MahLooL,98 implemented as a serverless bidirectional RNN model, achieves 70% overall accuracy in solubility prediction while excelling for short peptides (18–50 amino acids) with 91.3% accuracy and 0.95 AUROC. The model incorporates simulated gastric conditions, making it valuable for oral delivery assessments.
Stapled peptide development
Hydrocarbon-stapled peptides represent a promising therapeutic class requiring specialized computational approaches due to their unique structural constraints.151 StaPep, introduced in 2024, provides the first comprehensive toolkit for stapled peptide structure prediction and feature extraction.152 The Python-based platform supports multiple stapling chemistries and achieved 0.85 AUC for cell penetration prediction using a curated dataset of 201 stapled versus 384 linear peptides. Recent deep learning advances have significantly improved stapled peptide design capabilities. A recent study applying CNN and LSTM models to antimicrobial stapled peptides achieved perfect accuracy on test sets by combining AlphaFold structural predictions with sequence-based descriptors. Experimental validation confirmed robust bacterial inhibition, low hemolysis, and excellent serum stability for designed cysteine-and lysine-stapled peptides.153
Integrated screening platforms
Comprehensive screening platforms now integrate multiple prediction capabilities. Peptipedia v2.0 is a user-friendly database containing over 100 000 peptide sequences from 70+ databases, incorporating 90+ binary classification models for biological activity prediction. The platform combines traditional sequence analysis with machine learning predictions, enabling comprehensive candidate evaluation.154 Top-ML shows interpretable machine learning approaches for anticancer peptide classification, employing topological features to achieve state-of-the-art performance while providing interpretable insights into structure–activity relationships.155 The Extra-Trees classifier-based model outperforms existing deep learning approaches on benchmark datasets while offering greater transparency in prediction rationale.
Clinical and translational context
Clinical success in the peptide therapies has been transformative across multiple areas. For example, Tirzepatide was approved by the FDA for type 2 diabetes,156 and Zepbound for obesity.157 Various peptide-based therapies, such as Survodutide (Phase 3) for obesity158 and Glepaglutide (registration phase) for short bowel syndrome,159 have been under clinical trial. Besides, radiolabeled peptides such as Pluvicto, approved in 2022 for metastatic castration resistant prostate cancer therapy.160 Recent clinical successes on the oral formulation of Semaglutide using the absorption enhancer SNAC displayed a practical oral medicine, expanding patient access and adherence while preserving pharmacodynamic benefit.161 Constrained peptides and stapled designs that enhance cell permeability and helicity are subjected to oncology trials, as demonstrated by ALRN-6924. This stabilized a-helical peptide targets the p53 regulatory axis and has shown tolerability and pharmacodynamic activity in early-phase studies.162
Peptide delivery is now clinically validated for selected molecules using permeation enhancers and promoters. At the same time, other formulations include the use of nanoparticles and conjugate strategies to address the target specificity, and intracellular administration has been tested in clinical trials.163 The growing use of non-canonical amino acids and peptidomimetics augments the stability and membrane permeability of the peptide-based therapeutics. However, these modifications pose a significant challenge since they could alter the metabolic pathways and safety profile.164 The development of translation has seen significant advances with AI.165 The generative structure prediction and design with potentially peptide candidate selection and prediction towards the physicochemical properties have accelerated the peptides-based drug discovery pipeline.166,167 Nevertheless, translating the peptide design from computational platform to preclinical and FDA approval requires a coordinated timeline and clinical strategy that, upon aligning for the well-suited study, could pave the way towards more compelling solutions for drug discovery.
Bridging computational predictions and therapeutic efficacy: critical gaps and emerging solutions
AI-driven peptide design has yielded notable outcomes, including predicted binding affinities in the nanomolar range and remarkable structural accuracy. Numerous computational tools spanning deep learning architectures, generative models, and therapeutic screening platforms have been developed in recent years to address distinct phases of peptide drug discovery (Table 1). However, despite this architectural diversity and experimental successes, a fundamental disconnect persists between these in silico predictions and therapeutic efficacy in clinical testing.168 We discuss some of the critical parameters that serve as crucial barriers and emerging solutions to bridge the gap between computational and clinical perspectives.
Table 1.
Comprehensive overview of AI-based tools for peptide-based drug discovery
| Tool/model | Architecture/type | Primary application |
|---|---|---|
| PepCNN | Convolutional neural network | Peptide-binding residue prediction |
| CAMP | Multi-channel CNN | Multi-level peptide–protein interaction prediction |
| Cross-TCR-interpreter | Transformer | TCR-pMHC interaction prediction |
| ABTrans | Transformer | Peptide–antibody interaction |
| PepNN | Graph attention network | Sequence & structure-based PPI prediction |
| TPepPro | Transformer | Peptide–protein interaction prediction |
| TP-LMMSG | Hierarchical Multi-scale residual network + PLM | Therapeutic peptide prediction (AMP/AVP/ACP) |
| PepBCL | BERT-based | Peptide-binding residue prediction |
| PeptideBERT | BERT-based | Peptide property prediction |
| VAE-MH | VAE + Metropolis–Hastings | Peptide extension design |
| PepVAE | Semi-supervised VAE | Antimicrobial peptide generation |
| HydrAMP | Conditional VAE | Antimicrobial peptide design |
| PandoraGAN | GAN (LeakGAN-based) | Antiviral peptide generation |
| AMPGAN v2 | Bidirectional conditional GAN | Antimicrobial peptide design |
| HelixGAN | GAN for structural design | De novo alpha-helix structural design |
| RFdiffusion | Diffusion model | Protein/peptide backbone generation |
| RFpeptides | Diffusion model for cyclic peptides | Cyclic peptide design |
| PepGLAD | Geometric latent diffusion | All-atom peptide generation |
| ProteinMPNN | Message-passing neural network | Sequence design from fixed backbones |
| DiffPepBuilder | SE(3)-equivariant diffusion | De novo peptide binder design |
| HYDRA | Target-aware diffusion | Target-aware peptide generation |
| PepTune | Masked diffusion + MCTS guidance | Multi-objective peptide optimization |
| AMPGen | Autoregressive diffusion + XGBoost + LSTM | Antimicrobial peptide generation |
| CPL-Diff | Mask-controlled diffusion | Length-tunable peptide generation |
| MMCD | Multimodal contrastive diffusion | Multimodal peptide generation |
| ProtFlow | Flow matching on compressed PLM embeddings | Fast peptide/antibody generation |
| HelixFlow | SE(3)-equivariant flow matching | Full-atom helical peptide design |
| NCFlow | Flow matching with non-canonical residues | Non-canonical amino acid incorporation |
| BindCraft | AlphaFold2-based hallucination | One-shot peptide binder design |
| PepMLM | Masked language model (fine-tuned ESM-2) | Target-conditioned peptide generation |
| AMP-Designer | Large language model | Rapid AMP design |
| LigandMPNN | Message-passing neural network (with ligands) | Sequence design with ligand context |
| CYC_BUILDER | Monte Carlo tree search + RL | Target-specific cyclic peptide design |
| PepINVENT | Sequence generator for non-canonical AAs | Non-natural amino acid exploration |
| AlphaFold3 | Structure prediction (modified residues) | Complex modeling with modified residues |
| Chroma | Programmable generative model | Constrained protein/peptide generation |
| AMP-Diffusion | Latent diffusion model + PLM | Broad-spectrum AMP generation |
| ToxGIN | Graph isomorphism Network | Peptide toxicity prediction |
| tAMPer | Multimodal GNN + RNN (ESM-2 + ColabFold) | Structure-aware toxicity prediction |
| UnifyImmun | Transformer + Cross-attention | Immunogenicity prediction (HLA/TCR) |
| MahLooL | Bidirectional RNN | Peptide solubility prediction |
| StaPep | Structure prediction + Feature extraction | Stapled peptide structure prediction |
| Peptipedia v2.0 | Database + ML models | Comprehensive peptide screening |
| Top-ML | Classifier | Anticancer peptide classification |
The metrics-reality gap
Current AI-based models demonstrate considerable efficiency in predicting the binding affinities, structural stabilities, and AMP (toxic vs nontoxic) binary classification properties. However, the therapeutic efficacy is multifactorial, determined by various biological processes that extend far beyond simple parameters and features.169 While AI/ML models achieve an AUROC value exceeding 0.95 in determining the AMPs, they may provide limited transferability in in vivo efficacy against distinct bacterial strains under varying physiological conditions, such as pH and ionic strength.170 This disparity highlights the importance of bridging computational model development with experimental outcomes.
Off-target effects and selectivity
While computational predictions show success in estimating affinity for the designated targets, they also account for binding to off-targets. A critical gap that needs to be considered, which could lead to adverse effects.171 In therapeutic peptide development, selectivity is relatively important; however, most computational screening pipelines prioritize binding over adequately assessing specificity.172 Thus, complex models still struggle to distinguish between the highly selective binders and off-targets that would lead to toxicity in clinical testing.
Metabolic stability and pharmacokinetics
The half-life of peptides in physiological conditions often depends on a complex network of degradation pathways.173 The current AI-based models solely rely on the predominance of in vivo assay data collected under controlled pH and other factors, with limited resemblance to the dynamic enzymatic environment. Recent studies have integrated the enzymatic cleavage site prediction and transfer learning approach to augment model performance,174 but these approaches remain constrained by the shortage of pharmacokinetic data. Thus, a persistent gap still prevails between the predicted structural stability and the observed serum half-life, which continues to hinder the translation of computationally designed peptides into viable clinical candidates.
Immunogenicity and immune system interactions
The immunogenic response poses one of the most significant yet least predictable challenges in peptide-based drug development.175 Although deep learning-based models for predicting T-cell epitopes and MHC binding have made substantial progress,176 they fail to capture the full complexity of adaptive immune responses. For instance, various factors, such as peptide aggregation propensity, PTSMs, and patient-specific HLA, influence immunogenicity that current models cannot fully capture.177 While discrepancies regarding progress towards the immune response or failed peptides are minimally reported. This prompts the model to focus solely on predictions that are overly optimistic and have a limited capacity to identify novel designs.
Physiological and microenvironmental factors
Peptide activity is highly dependent on various factors, including pH, ionic strength, lipid composition, and the presence of serum proteins.178 AMPs that demonstrate potent activity against specific bacterial strains in standardized laboratories may lose efficacy in distinct microenvironments or when exposed to competitive host defense peptides.179 Similarly, anticancer peptides optimized for binding to target proteins often exhibit reduced activity when competing with serum proteins or navigating the extracellular compartments.180 These failures of the designed peptides often emerge from the limited training datasets. The scarcity of the training datasets spanning diverse microenvironments limits the model’s generalizability and predictive power.
ADMET prediction limitations
Machine learning and AI-based studies addressing ADMET predictions have focused on critical gaps through reinforcement learning frameworks that jointly predict various endpoints, such as membrane permeability and stability.181 Despite these advances in the AI/ML framework, the current models still remain constrained by biased training datasets and poor generalization to novel peptides. Critical properties such as bioavailability and drug–drug interactions, as well as the dose–response relationship, still remain inadequately modeled, which hinders the accurate prediction of clinical translatability and optimal dose regimen.182 ADMET prediction will represent a significant opportunity until these critical gaps are addressed through experimental data collection and an improved modeling framework in computational peptide design.
Data quality and bias limitations
The quality of the training dataset represents a critical yet underappreciated limitation in the AI design.183 Currently, most datasets in repositories are evaluated under narrow experimental conditions, such as specific strains, pH levels, ionic strengths, and defined cell lines. Models trained on those datasets achieve good performance but fail to yield better results on different experimental models or physiological conditions. Furthermore, the reported data from articles are biased towards positive results, which often overshadow the negative results and inactive compounds.184-186
Practical barriers to accessibility and equity
With the current developments in AI-based tools, another important factor to be monitored is the use of infrastructure for training computational models. This might be a barrier to upcoming scientific discoveries, as the size of the datasets is increasing exponentially.187 Training advanced models demands high-performance computing resources and specialized software environments, thereby forming a hurdle to model development. The disparity in resources has also been observed in the research ecosystems, where the expert AI models remain concentrated in a small, well-resourced group. Such barriers may hinder innovations in peptide therapeutics by limiting participation from research groups with limited resources in developing countries.188 Additionally, cloud-based platforms offer an alternative to this barrier, but they still incur costs that accumulate exponentially for intensive calculations. Furthermore, data privacy concerns persist when working with proprietary compounds, and the long-term use of these platforms remains uncertain.189
Risks associated with generative AI in peptide design
Current advances in generative deep-learning architectures for peptide design enable rapid development of novel peptides. However, these models tend to introduce a set of restraints that limit the translation from computational design to clinically effective therapeutics. For instance, one of the key factors is the “hallucination” where LLM models generate peptide sequences that are biophysically unstable and synthetically infeasible.190 Moreover, the generative AI models frequently struggle to represent intrinsically disordered peptides (IDPs) or sequences that adopt ensemble states. This occurs due to training dataset bias, which heavily relies on rigid structures, thereby failing to capture the dynamic flexibility required for many biological interactions.191 Most importantly, the development of peptides using generative AI, when misused, poses a significant risk to biosecurity and raises concerns about dual use. These could lead to the development of peptides with biologically harmful properties. A recent study demonstrated that AI can develop peptides that bind to the toxic substance while diverging sufficiently in their primary sequence to evade existing screening protocols.192 This vulnerability poses a significant oversight in current biosafety frameworks and needs improved governance strategies.
Emerging solutions to bridge the gap between computational and experimental studies
Bridging the gap between computational and experimental outcomes requires a paradigm shift in the AI model development, training, and validation. With the current development of optimizations, multiple-objective training, and physics-based machine learning models ensure mechanical soundness. Further, incorporating feedback and continuous adaptation approaches for computational validation would advance AI-driven peptide design. Here, we report some recent advances in AI that could aid in bridging the gap between computational and experimental studies.
Agentic AI: adaptive multi-agent frameworks
A new generation of AI agents has emerged that differs from the conventional predictive models. Rather than operating as static pattern recognizers trained on a fixed dataset, agentic AI systems dynamically incorporate new experimental data and adapt to unexpected outcomes and reasons across diverse knowledge domains without requiring complete retraining.193 These systems employ multi-agent architectures, where AI components cooperate with distinct expertise to collaborate autonomously and work together as a cohesive system.194
The ProtAgents framework demonstrates this approach by deploying multiple large language-based model agents, including a knowledge retrieval specialist, a structural analysis module, and a physics simulation engine that communicates through a structured protocol.195 When designing peptides, these agents collaboratively integrate literature knowledge, predict structures, simulate binding interactions, and evaluate the outcomes. Critically, these agents can incorporate experimental results in real-time, adjusting their design strategy without retraining from scratch.
The Sparks system has further extended these concepts by implementing paired generation and reflection agents that autonomously brainstorm hypotheses, design computational experiments, execute simulations, and iteratively refine designs based on the outcomes.196 Sparks has successfully identified unknown structure–property relationships in the protein mechanism, thus suggesting that these agentic systems can generate significant insights rather than merely interpolating existing knowledge. Besides the ability of the agentic system to reason about tradeoff balancing conflicts, such as binding affinity versus toxicity or stability versus permeability, this shows a significant advance over the optimization approach.197
Chain-of-thought reasoning models
Conventional deep learning networks operate as black boxes, making predictions without providing any justification for their decisions.198 The chain of thought reasoning models decompose complex design problems into logical steps, making their decision-making process interpretable.199 This provides adequate information when the prediction fails experimentally, which aids the researchers in tracing back the reasoning chain to identify the missing considerations.
The PepThink-R1 model demonstrates this approach for cyclic peptide optimization by combining chain-of-thought reasoning with supervised fine-tuning and reinforcement learning. PepThink-R1 improved peptide sequences and explained the reasoning behind the modifications that enhance stability.200 This enables the researchers to critically evaluate the AI suggestion and make informed decisions about the predicted candidate peptides before the experimental validation.
The ether0 model, which was trained on 24 billion parameters specifically for chemistry using reinforcement learning on more than 600 000 experimentally grounded problems spanning synthesizability, blood–brain barrier permeability, and metabolic activity. This model was designed to reason through multistep chemical problems using deterministic verification of correct vs incorrect solutions. The model’s performance excels in specialized chemistry models and aligns with human experts in molecular design.201
The PRefLexOR framework demonstrates the preferencebased recursive language models that can train AI systems to reason through multi-step scientific problems with explicit reflection and corrections.202 PRefLexOR introduces thinking tokens that mark reasoning phases, enabling models to evaluate the reasoning strategy and regenerate improved solutions through refined cycles. Hence, the training methodology in these reasoning models represents a paradigm shift. Rather than relying on the pretraining datasets, these systems learn to reason through the problem using reinforcement learning. Thus, for peptide drug design specifically, chain-of-thought reasoning provides critical advantages in overcoming the barriers of data scarcity and the knowledge gap.
Physics-based data-driven models
The recent advancement in hybrid frameworks that combine the mechanistic insight of MD simulations with the scalability of ML. The physics-based force field developed in the MD simulations accurately captures the properties of proteins and peptides, including atomic interactions and thermodynamic behavior, which are often inaccessible to data-driven methods constrained by limited datasets. In turn, the ML models aid in extracting the complex interactions and patterns from the MD simulation data.203,204 Recently, physics-informed training, where the MD-generated conformational ensembles and trajectories serve as training inputs, enables the complete pattern to be derived from simulation-derived data.205 Active learning workflows further enhance efficiency by allowing ML models to validate top predictions and refine model accuracy.206 This iterative, closed-loop integration not only preserves physical rigor but also accelerates discovery, offering a balanced and pragmatic path toward more predictive and experimentally relevant peptide design.
Ethical considerations in AI-driven peptide drug design
The development of AI in drug discovery raises a key challenge in terms of transparency, accountability, and bias.207 Due to the limited interpretability of the model and the complexity of the training data, which tends to yield specific positive results, this raises concerns about the therapeutic outcomes. On the other hand, the misuse of AI-based technology for harmful purposes underscores the need for the development of stringent government policies and robust ethical oversigh.208 In the absence of a harmonized regulatory framework, the need to develop transparency and sustainability with ethically responsible AI is a key factor to consider in the development of peptide-based rugs, with future directions toward clinical translation.
Challenges, opportunities, and future perspectives
Peptide-based therapeutics stand at the forefront, where AI-driven innovations accelerate drug discovery and development. Yet, the AI-based models still face significant hurdles in design and development. Data quality is a vital factor that provides the foundation for the AI models. Databases such as APd3 and CAMPR3 are curated for specific species, structural classes (alpha-helix, beta-sheet), and assay conditions. However, these databases are limited to non-canonical amino acids, stapled peptides, and d-amino acid modifications that have been proven to exhibit clinical importance. Model interpretability presents a significant challenge, as the need to explain AI models in drug discovery studies becomes essential for both scientific and regulatory acceptance.
The critical challenge in the AI-based peptide design is the limited use of empirical knowledge from chemistry and physics-based principles. Most models currently developed rely on statistical associations with the training dataset rather than thermodynamic and physicochemical properties that determine key properties, such as folding and proteolytic stability. Various studies have highlighted that peptide efficacy is strongly dependent on the physicochemical landscape, and that purely data-driven models frequently fail to capture in determining peptide efficacy.209 However, emerging hybrid frameworks are bridging this gap by integrating molecular simulation data and quantum chemistry-derived potentials. These physics-informed strategies have demonstrated robustness for complex targets, including macrocyclic peptides, by effectively filtering ‘hallucinations’ and unrealistic conformational states.210,211 Thus, embedding first-principles physics into generative pipelines represents a criterion for the future of reliable peptide-based therapeutics.
The integration of advanced computational approaches into peptide-based drug discovery offers immense opportunities. The use of deep learning models shows potential to enhance predictive accuracy in peptide stability, pharmacokinetics, and interactions, thereby facilitating rational design to improve efficacy. Integrating multiple AI approaches through ensemble methods and consensus scoring represents a promising direction for future research. From a scientific viewpoint, peptide applications are rapidly expanding beyond therapeutics to include biomaterials, diagnostics, and synthetic biology. However, this therapeutic expansion requires addressing the current challenges and the future directions. We can emphasize that the integration of algorithmic innovations and experimental validation in these domains accelerates the efficiency of the drug discovery pipeline, bridging the gap towards clinical and industrial implementations.
Acknowledgements
We acknowledge support from the NIH grant 1R35GM134864 (to N. V. D.)
Biographies
Srinivasan Ekambaram is a postdoctoral fellow in Prof. Nikolay Dokholyan’s lab at the University of Virginia. He earned his MS (2017) and PhD (2021) in Computational Biology from the Vellore Institute of Technology, Vellore, under the supervision of Dr R. Rajasekaran. Following his doctoral studies, he served as an Institute Postdoctoral Fellow in Dr Anand Srivastava’s group at the Indian Institute of Science, Bengaluru. His research focuses on designing peptide-based inhibitors for disordered proteins, developing deep learning models for protein structure and function, and applying large language models (LLMs) for precision medicine.
Dr Nikolay Dokholyan received his PhD in Physics from Boston University in 1999 under the supervision of Dr H. Eugene Stanley. He was then an NIH NRSA Fellow at Harvard University in Dr Eugene Shakhnovich’s lab, focusing on protein folding, design, and evolution. Dr Dokholyan held faculty positions at the University of North Carolina at Chapel Hill (2002–2017) and Penn State College of Medicine (2018–2025) before joining the University of Virginia. His research involves developing computational methods to engineer molecular structures and applying these tools to understand human diseases and create therapeutic strategies. Dr Dokholyan has held several endowed positions, and has been elected to be a Fellow of the American Physical Society (2012), the American Association for the Advancement of Science (2019), and the American Institute for Medical and Biological Engineering (2022).
Footnotes
Conflicts of interest
There are no conflicts to declare.
Data availability
No primary research results, software, or code have been included, and no new data were generated or analyzed as part of this review.
References
- 1.Wang L, Wang N, Zhang W, Cheng X, Yan Z, Shao G, Wang X, Wang R and Fu C, Signal Transduction Targeted Ther., 2022, 7, 48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Xiao W, Jiang W, Chen Z, Huang Y, Mao J, Zheng W, Hu Y and Shi J, Signal Transduction Targeted Ther., 2025, 10, 74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Scott DA and Best CH, Ind. Eng. Chem, 1925, 17, 238–240. [Google Scholar]
- 4.Fosgerau K and Hoffmann T, Drug Discovery Today, 2015, 20, 122–128. [DOI] [PubMed] [Google Scholar]
- 5.Al Musaimi O, J. Pept. Sci, 2024, 30, e3627. [DOI] [PubMed] [Google Scholar]
- 6.Shabsigh M and Solomon LA, Chem. Biomed. Imaging, 2024, 2, 615–630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Gupta S, Azadvari N and Hosseinzadeh P, BioDesign Res., 2022, 2022, 9783197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hoegen Dijkhof LR, Rönkkö TKE, von Vegesack HC, Lenzing J and Hauser AS, Briefings Bioinf., 2025, 26, bbaf186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lu F, Zhang X, Geng Y, Wang H and Tang J, Chem. Commun, 2024, 60, 7942–7945. [DOI] [PubMed] [Google Scholar]
- 10.Wynne C and Elmes RBP, Sens. Diagn, 2024, 3, 987–1013. [Google Scholar]
- 11.Lombardi L, Genio VD, Albericio F and Williams DR, Chem. Rev, 2025, 125, 7099–7166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Du Y, Li L, Zheng Y, Liu J, Gong J, Qiu Z, Li Y, Qiao J and Huo Y-X, Appl. Environ. Microbiol, 2022, 88, e01617–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zhu Q, Chen Z, Paul PK, Lu Y, Wu W and Qi J, Acta Pharm. Sin. B, 2021, 11, 2416–2448. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lamers C, Future Drug Discovery, 2022, 4, FDD75. [Google Scholar]
- 15.Southworth J, Migliaccio K, Glover J, Glover J, Reed D, McCarty C, Brendemuhl J and Thomas A, Comput. Educ.: Artif. Intell, 2023, 4, 100127. [Google Scholar]
- 16.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P and Hassabis D, Nature, 2021, 596, 583–589. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu C-W, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F and Zhang J, Innovation, 2021, 2, 100179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Elahi M, Afolaranmi SO, Martinez Lastra JL and Perez Garcia JA, Discov. Artif. Intell, 2023, 3, 43. [Google Scholar]
- 19.Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, dos Santos Costa A, Fazel-Zarandi M, Sercu T, Candido S and Rives A, Science, 2023, 379, 1123–1130. [DOI] [PubMed] [Google Scholar]
- 20.Sarker IH, SN Comput. Sci, 2021, 2, 420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y, arXiv, 2014, preprint, arXiv:1406.2661, DOI: 10.48550/arXiv.1406.2661. [DOI] [Google Scholar]
- 22.Yang L, Zhang Z, Song Y, Hong S, Xu R, Zhao Y, Zhang W, Cui B and Yang M-H, arXiv, 2024, preprint, arXiv:2209.00796, DOI: 10.48550/arXiv.2209.00796. [DOI] [Google Scholar]
- 23.Kingma DP and Welling M, FNT in Machine Learning, 2019, 12, 307–392. [Google Scholar]
- 24.Goles M, Daza A, Cabas-Mora G, Sarmiento-Varón L, Sepúlveda-Yañez J, Anvari-Kazemabad H, Davari MD, Uribe-Paredes R, Olivera-Nappa Á, Navarrete MA and Medina-Ortiz D, Briefings Bioinf., 2024, 25, bbae275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Singh S, Gupta H, Sharma P and Sahi S, Artif. Intell. Chem, 2024, 2, 100039. [Google Scholar]
- 26.Rettie SA, Campbell KV, Bera AK, Kang A, Kozlov S, Bueso YF, De La Cruz J, Ahlrichs M, Cheng S, Gerben SR, Lamb M, Murray A, Adebomi V, Zhou G, DiMaio F, Ovchinnikov S and Bhardwaj G, Nat. Commun, 2025, 16, 4730. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Wu C-H, Liu I-J, Lu R-M and Wu H-C, J. Biomed. Sci, 2016, 23, 8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Tucker AT, Leonard SP, DuBois CD, Knauf GA, Cunningham AL, Wilke CO, Trent MS and Davies BW, Cell, 2018, 172, 618–628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Jaroszewicz W, Morcinek-Orłowska J, Pierzynowska K, Gaffke L and Węgrzyn G, FEMS Microbiol. Rev, 2022, 46, fuab052. [DOI] [PubMed] [Google Scholar]
- 30.Sloth AB, Bakhshinejad B, Jensen M, Stavnsbjerg C, Liisberg MB, Rossing M and Kjaer A, Viruses, 2022, 14, 2402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Mondal A, Chang L and Perez A, QRB Discov., 2022, 3, e17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O and Kmiecik S, Drug Discovery Today, 2018, 23, 1530–1537. [DOI] [PubMed] [Google Scholar]
- 33.Kurcinski M, Pawel Ciemny M, Oleniecki T, Kuriata A, Badaczewska-Dawid AE, Kolinski A and Kmiecik S, Bioinformatics, 2019, 35, 4170–4172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Zhou P, Jin B, Li H and Huang S-Y, Nucleic Acids Res., 2018, 46, W443–W450. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Khramushin A, Ben-Aharon Z, Tsaban T, Varga JK, Avraham O and Schueler-Furman O, Proc. Natl. Acad. Sci. U. S. A, 2022, 119, e2121153119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Badaczewska-Dawid AE, Kmiecik S and Koliński M, Briefings Bioinf., 2021, 22, bbaa109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Agoni C, Fernández-Díaz R, Timmons PB, Adelfio A, Gómez H and Shields DC, Molecular Modelling in Bioactive Peptide Discovery and Characterisation, Biomolecules, 2025, 15(4), 524, DOI: 10.3390/biom1504052, (accessed August 21, 2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P and Tufféry P, Nucleic Acids Res., 2016, 44, W449–W454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Vincenzi M, Mercurio FA and Leone M, Int. J. Mol. Sci, 2024, 25, 1798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Xue B, Li R, Cheng Z and Zhou X, ACS Cent. Sci, 2024, 10, 2111–2118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Amarasinghe KN, De Maria L, Tyrchan C, Eriksson LA, Sadowski J and Petrović D, J. Chem. Inf. Model, 2022, 62, 2999–3007. [DOI] [PubMed] [Google Scholar]
- 42.Verma J, Khedkar VM and Coutinho EC, Curr. Top. Med. Chem, 2010, 10, 95–115. [DOI] [PubMed] [Google Scholar]
- 43.Nongonierma AB and FitzGerald RJ, RSC Adv., 2016, 6, 75400–75413. [Google Scholar]
- 44.Nielsen JC, Hjorringgaard C, Nygaard MM, Wester A, Elster L, Porsgaard T, Mikkelsen RB, Rasmussen S, Madsen AN, Schlein M, Vrang N, Rigbolt K and Dalbooge LS, J. Med. Chem, 2024, 67, 11814–11826. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.De Vivo M, Masetti M, Bottegoni G and Cavalli A, J. Med. Chem, 2016, 59, 4035–4061. [DOI] [PubMed] [Google Scholar]
- 46.Geng H, Chen F, Ye J and Jiang F, Comput. Struct. Biotechnol. J, 2019, 17, 1162–1170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Proctor EA and Dokholyan NV, Curr. Opin. Struct. Biol, 2016, 37, 9–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Srinivasan E and Rajasekaran R, J. Neurol. Sci, 2019, 405, 116425. [DOI] [PubMed] [Google Scholar]
- 49.Tuan Do A, Hai Nguyen T, Quan Pham M, Truong Nguyen H, Phuoc Long N, Van Vu V, Thu Phung HT and Tung Ngo S, RSC Adv., 2025, 15, 12866–12875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Moraca F, Vespoli I, Mastroianni D, Piscopo V, Gaglione R, Arciello A, Nisco MD, Pacifico S, Catalanotti B and Pedatella S, RSC Med. Chem, 2024, 15, 2286–2299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Chandrasekhar G, Srinivasan E, Nandhini S, Pravallika G, Sanjay G and Rajasekaran R, J. Biomol. Struct. Dyn, 2023, 0, 1–12. [DOI] [PubMed] [Google Scholar]
- 52.Dagliyan O, Proctor EA, D’Auria KM, Ding F and Dokholyan NV, Structure, 2011, 19, 1837–1845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Gomari MM, Arab SS, Balalaie S, Ramezanpour S, Hosseini A, Dokholyan NV and Tarighi P, Proteins, 2024, 92, 76–95. [DOI] [PubMed] [Google Scholar]
- 54.Hao J, Serohijos AWR, Newton G, Tassone G, Wang Z, Sgroi DC, Dokholyan NV and Basilion JP, PLoS Comput. Biol, 2008, 4, e1000138. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Ding F and Dokholyan NV, PLoS Comput. Biol, 2006, 2, e85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Clayton J, Baweja L and Wereszczynski J, Methods Mol. Biol, 2022, 2405, 151–167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Wang W, Phys. Chem. Chem. Phys, 2021, 23, 777–784. [DOI] [PubMed] [Google Scholar]
- 58.Appadurai R, Nagesh J and Srivastava A, Nat. Commun, 2021, 12, 958. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Salmaso V, Sturlese M, Cuzzolin A and Moro S, Structure, 2017, 25, 655–662. [DOI] [PubMed] [Google Scholar]
- 60.Ouyang J, Sheng Y and Wang W, Cells, 2022, 11, 4016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Agoni C, Fernández-Díaz R, Timmons PB, Adelfio A, Gómez H and Shields DC, Biomolecules, 2025, 15, 524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Senthilkumar B, Meshach Paul D, Srinivasan E and Rajasekaran R, J. Cluster Sci, 2017, 28, 2549–2563. [Google Scholar]
- 63.Sekar PC, Paul DM, Srinivasan E and Rajasekaran R, J. Mol. Model, 2021, 27(1), 10. [DOI] [PubMed] [Google Scholar]
- 64.Sekar PC, Srinivasan E, Chandrasekhar G, Paul DM, Sanjay G, Surya S, Kumar NSAR and Rajasekaran R, J. Mol. Model, 2022, 28, 128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Ding F, Tsao D, Nie H and Dokholyan NV, Structure, 2008, 16, 1010–1018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Proctor EA and Dokholyan NV, Curr. Opin. Struct. Biol, 2016, 37, 9–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Peng S, Ding F, Urbanc B, Buldyrev SV, Cruz L, Stanley HE and Dokholyan NV, Phys. Rev. E: Stat., Nonlinear, Soft Matter Phys, 2004, 69, 041908. [DOI] [PubMed] [Google Scholar]
- 68.Barton S, Jacak R, Khare SD, Ding F and Dokholyan NV, J. Biol. Chem, 2007, 282, 25487–25492. [DOI] [PubMed] [Google Scholar]
- 69.Ding F, Borreguero JM, Buldyrey SV, Stanley HE and Dokholyan NV, Proteins, 2003, 53, 220–228. [DOI] [PubMed] [Google Scholar]
- 70.Khare SD and Dokholyan NV, Curr. Protein Pept. Sci, 2007, 8, 573–579. [DOI] [PubMed] [Google Scholar]
- 71.Tsao D and Dokholyan NV, Phys. Chem. Chem. Phys, 2010, 12, 3491–3500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Chandrasekhar G and Rajasekaran R, Int. J. Pept. Res. Ther, 2021, 27, 1555–1575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Sekar PC and Rajasekaran R, Int. J. Pept. Res. Ther, 2021, 27, 1043–1056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Hao J, Serohijos AWR, Newton G, Tassone G, Wang Z, Sgroi DC, Dokholyan NV and Basilion JP, PLoS Comput. Biol, 2008, 4, e1000138. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Gomari MM, Arab SS, Balalaie S, Ramezanpour S, Hosseini A, Dokholyan NV and Tarighi P, Proteins, 2024, 92, 76–95. [DOI] [PubMed] [Google Scholar]
- 76.Coppa C, Bazzoli A, Barkhordari M and Contini A, J. Chem. Inf. Model, 2023, 63, 3030–3042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Miao J, Ghosh AP, Ho MN, Li C, Huang X, Pentelute BL, Baleja JD and Lin Y-S, J. Phys. Chem. B, 2024, 128, 5281–5292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Amundarain MJ, Vietri A, Dodero VI and Costabel MD, J. Phys. Chem. B, 2023, 127, 2407–2417. [DOI] [PubMed] [Google Scholar]
- 79.Robustelli P, Piana S and Shaw DE, Proc. Natl. Acad. Sci. U. S. A, 2018, 115, E4758–E4766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot BL, Grubmüller H and MacKerell AD, Nat. Methods, 2017, 14, 71–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Hou T, Wang J, Li Y and Wang W, J. Chem. Inf. Model, 2011, 51, 69–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Rossino G, Marchese E, Galli G, Verde F, Finizio M, Serra M, Linciano P and Collina S, Molecules, 2023, 28, 7165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, Bodenstein SW, Evans DA, Hung C-C, O’Neill M, Reiman D, Tunyasuvunakool K, Wu Z, ZŽmgulytė A, Arvaniti E, Beattie C, Bertolli O, Bridgland A, Cherepanov A, Congreve M, Cowen-Rivers AI, Cowie A, Figurnov M, Fuchs FB, Gladman H, Jain R, Khan YA, Low CMR, Perlin K, Potapenko A, Savy P, Singh S, Stecula A, Thillaisundaram A, Tong C, Yakneen S, Zhong ED, Zielinski M, ZŽídek A, Bapst V, Kohli P, Jaderberg M, Hassabis D and Jumper JM, Nature, 2024, 630, 493–500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.McDonald EF, Jones T, Plate L, Meiler J and Gulsevin A, Structure, 2023, 31, 111–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, Millán C, Park H, Adams C, Glassman CR, DeGiovanni A, Pereira JH, Rodrigues AV, van Dijk AA, Ebrecht AC, Opperman DJ, Sagmeister T, Buhlheller C, Pavkov-Keller T, Rathinaswamy MK, Dalwadi U, Yip CK, Burke JE, Garcia KC, Grishin NV, Adams PD, Read RJ and Baker D, Science, 2021, 373, 871–876. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Baek M, McHugh R, Anishchenko I, Jiang H, Baker D and DiMaio F, Nat. Methods, 2024, 21, 117–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M, Nat. Methods, 2022, 19, 679–682. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Mou M, Zhang Z, Pan Z and Zhu F, Research, 2025, 8, 0615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Chandra A, Sharma A, Dehzangi I, Tsunoda T and Sattar A, Sci. Rep, 2023, 13, 20882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Lei Y, Li S, Liu Z, Wan F, Tian T, Li S, Zhao D and Zeng J, Nat. Commun, 2021, 12, 5465. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Koyama K, Hashimoto K, Nagao C and Mizuguchi K, Front. Bioinform, 2023, 3, 1274599. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Abdin O, Nim S, Wen H and Kim PM, Commun. Biol, 2022, 5, 503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Jin X, Chen Z, Yu D, Jiang Q, Chen Z, Yan B, Qin J, Liu Y and Wang J, Bioinformatics, 2024, 41, btae708. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Pancino N, Gallegati C, Romagnoli F, Bongini P and Bianchini M, Int. J. Mol. Sci, 2024, 25, 5870. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Chen N, Yu J, Zhe L, Wang F, Li X and Wong K-C, Briefings Bioinf., 2024, 25, bbae308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96.Jha K, Saha S and Singh H, Sci. Rep, 2022, 12, 8360. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Jha K, Karmakar S and Saha S, Sci. Rep, 2023, 13, 5663. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Ansari M and White AD, J. Chem. Inf. Model, 2023, 63, 2546–2553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Mao J, Guan S, Chen Y, Zeb A, Sun Q, Lu R, Dong J, Wang J and Cao D, Comput. Struct. Biotechnol. J, 2023, 21, 463–471. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Papanikolaou A, Sivtsov V, Zereik E, Ruggiero E, Bonini C and Bonsignorio F, bioRxiv, 2025, preprint, DOI: 10.1101/2025.03.18.643937. [DOI] [Google Scholar]
- 101.Wang R, Nakai K and Wei L, Methods Mol. Biol, 2025, 2941, 269–278. [DOI] [PubMed] [Google Scholar]
- 102.Guntuboina C, Das A, Mollaei P, Kim S and Barati Farimani A, J. Phys. Chem. Lett, 2023, 14, 10427–10434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 103.Siwek JC, Omelchenko AA, Chhibbar P, Arshad S, Rosengart A, Nazarali I, Patel A, Nazarali K, Rahimikollu J, Tilstra JS, Shlomchik MJ, Koes DR, Joglekar AV and Das J, Nat. Methods, 2025, 22, 1707–1719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.Chen S, Lin T, Basu R, Ritchey J, Wang S, Luo Y, Li X, Pei D, Kara LB and Cheng X, Nat. Commun, 2024, 15, 1611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 105.Dean SN and Walper SA, ACS Omega, 2020, 5, 20746–20754. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Dean SN, Alvarez JAE, Zabetakis D, Walper SA and Malanoski AP, Front. Microbiol, 2021, 12, 725727. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Szymczak P, Możejko M, Grzegorzek T, Jurczak R, Bauer M, Neubauer D, Sikora K, Michalski M, Sroka J, Setny P, Kamysz W and Szczurek E, Nat. Commun, 2023, 14, 1453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 108.Surana S, Arora P, Singh D, Sahasrabuddhe D and Valadi J, SN Comput. Sci, 2023, 4, 607. [Google Scholar]
- 109.Van Oort CM, Ferrell JB, Remington JM, Wshah S and Li J, J. Chem. Inf. Model, 2021, 61, 2198–2207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Xie X, Valiente PA and Kim PM, Bioinformatics, 2023, 39, btad036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Watson JL, Juergens D, Bennett NR, Trippe BL, Yim J, Eisenach HE, Ahern W, Borst AJ, Ragotte RJ, Milles LF, Wicky BIM, Hanikel N, Pellock SJ, Courbet A, Sheffler W, Wang J, Venkatesh P, Sappington I, Torres SV, Lauko A, De Bortoli V, Mathieu E, Ovchinnikov S, Barzilay R, Jaakkola TS, DiMaio F, Baek M and Baker D, Nature, 2023, 620, 1089–1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Vázquez Torres S, Leung PJY, Venkatesh P, Lutz ID, Hink F, Huynh H-H, Becker J, Yeh AH-W, Juergens D, Bennett NR, Hoofnagle AN, Huang E, MacCoss MJ, Expòsit M, Lee GR, Bera AK, Kang A, De La Cruz J, Levine PM, Li X, Lamb M, Gerben SR, Murray A, Heine P, Korkmaz EN, Nivala J, Stewart L, Watson JL, Rogers JM and Baker D, Nature, 2024, 626, 435–442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Rettie SA, Juergens D, Adebomi V, Bueso YF, Zhao Q, Leveille AN, Liu A, Bera AK, Wilms JA, Üffing A, Kang A, Brackenbrough E, Lamb M, Gerben SR, Murray A, Levine PM, Schneider M, Vasireddy V, Ovchinnikov S, Weiergräber OH, Willbold D, Kritzer JA, Mougous JD, Baker D, DiMaio F and Bhardwaj G, bioRxiv, 2024, preprint, DOI: 10.1101/2024.11.18.622547. [DOI] [Google Scholar]
- 114.Kong X, Jia Y, Huang W and Liu Y, arXiv, 2024, preprint, arXiv:2402.13555, DOI: 10.48550/arXiv.2402.13555. [DOI] [Google Scholar]
- 115.Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, Milles LF, Wicky BIM, Courbet A, de Haas RJ, Bethel N, Leung PJY, Huddy TF, Pellock S, Tischer D, Chan F, Koepnick B, Nguyen H, Kang A, Sankaran B, Bera AK, King NP and Baker D, Science, 2022, 378, 49–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 116.Wang F, Wang Y, Feng L, Zhang C and Lai L, J. Chem. Inf. Model, 2024, 64, 9135–9149. [DOI] [PubMed] [Google Scholar]
- 117.V. S. R, Choudhuri S and Ghosh B, J. Chem. Inf. Model, 2024, 64, 6912–6925. [DOI] [PubMed] [Google Scholar]
- 118.Tang S, Zhang Y and Chatterjee P, arXiv, 2025, arXiv:2412. 17780v4, , DOI: 10.48550/2412.17780v4. [DOI] [Google Scholar]
- 119.Jin S, Zeng Z, Xiong X, Huang B, Tang L, Wang H, Ma X, Tang X, Shao G, Huang X and Lin F, Commun. Biol, 2025, 8, 839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.Luo Z, Geng A, Wei L, Zou Q, Cui F and Zhang Z, Adv. Sci, 2025, 12, 2412926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 121.Wang Y, Liu X, Huang F, Xiong Z and Zhang W, arXiv, 2024, preprint, arXiv:2312.15665, DOI: 10.48550/arXiv.2312.15665. [DOI] [Google Scholar]
- 122.Li Z, Zeng Z, Lin X, Fang F, Qu Y, Xu Z, Liu Z, Ning X, Wei T, Liu G, Tong H and He J, arXiv, 2025, preprint, arXiv:2507.17731, DOI: 10.48550/arXiv.2507.17731. [DOI] [Google Scholar]
- 123.Lipman Y, Chen RTQ, Ben-Hamu H, Nickel M and Le M, arXiv, 2023, preprint, arXiv:2210.02747, DOI: 10.48550/arXiv.2210.02747. [DOI] [Google Scholar]
- 124.Kong Z, Zhu Y, Xu Y, Zhou H, Yin M, Wu J, Xu H, Hsieh C-Y, Hou T and Wu J, arXiv, 2025, preprint, arXiv:2504.10983, DOI: 10.48550/arXiv.2504.10983. [DOI] [Google Scholar]
- 125.Xie X, Valiente PA, Kim J, Lee JS and Kim PM. [Google Scholar]
- 126.Lee JS and Kim PM, bioRxiv, 2025, preprint, DOI: 10.1101/2025.07.31.667780. [DOI] [Google Scholar]
- 127.Pacesa M, Nickel L, Schellhaas C, Schmidt J, Pyatova E, Kissling L, Barendse P, Choudhury J, Kapoor S, Alcaraz-Serna A, Cho Y, Ghamary KH, Vinué L, Yachnin BJ, Wollacott AM, Buckley S, Westphal AH, Lindhoud S, Georgeon S, Goverde CA, Hatzopoulos GN, Gönczy P, Muller YD, Schwank G, Swarts DC, Vecchio AJ, Schneider BL, Ovchinnikov S and Correia BE, Nature, 2025, 646, 483–492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 128.Filius M, Patsos T, Minee H, Turco G, Liu J, Gnatzy M, Rooth RSM, Liu ACH, Ta RDT, Rijk IHA, Ziani S, Boxman FJ and Pomplun SJ, bioRxiv, 2025, preprint, DOI: 10.1101/2025.07.23.666285. [DOI] [Google Scholar]
- 129.Chen T, Dumas M, Watson R, Vincoff S, Peng C, Zhao L, Hong L, Pertsemlidis S, Shaepers-Cheu M, Wang TZ, Srijay D, Monticello C, Vure P, Pulugurta R, Kholina K, Goel S, DeLisa MP, Truant R, Aguilar HC and Chatterjee P, arXiv, 2024, preprint, arXiv:2310.03842v3, DOI: 10.48550/2310.03842v3. [DOI] [Google Scholar]
- 130.Bhat S, Palepu K, Hong L, Mao J, Ye T, Iyer R, Zhao L, Chen T, Vincoff S, Watson R, Wang TZ, Srijay D, Kavirayuni VS, Kholina K, Goel S, Vure P, Deshpande AJ, Soderling SH, DeLisa MP and Chatterjee P, Sci. Adv, 2025, 11, eadr8638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 131.Wang J, Feng J, Kang Y, Pan P, Ge J, Wang Y, Wang M, Wu Z, Zhang X, Yu J, Zhang X, Wang T, Wen L, Yan G, Deng Y, Shi H, Hsieh C-Y, Jiang Z and Hou T, Sci. Adv, 2025, 11, eads8932. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 132.Dauparas J, Lee GR, Pecoraro R, An L, Anishchenko I, Glasscock C and Baker D, Nat. Methods, 2025, 22, 717–723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Wang F, Zhang T, Zhu J, Zhang X, Zhang C and Lai L, Reinforcement Learning-Based Target-Specific De Novo Design of Cyclic Peptide Binders, J. Med. Chem, 2025, 68(16), 17287–17302, DOI: 10.1021/acs.jmedchem.5c00789, (accessed August 22, 2025). [DOI] [PubMed] [Google Scholar]
- 134.Wang Q, Hu X, Wei Z, Lu H and Liu H, Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides, Briefings Bioinf., 2024, 25(5), bbae444. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Geylan G, Janet JP, Tibo A, He J, Patronov A, Kabeshov M, Czechtizky W, David F, Engkvist O and De Maria L, Chem. Sci, 2025, 16, 8682–8696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 136.Zhang C, Wang W, Zhu N, Cao Z, Mao Q, Zhu C, Zhang C, Guo J and Duan H, bioRxiv, 2025, preprint, DOI: 10.1101/2025.05.24.655528. [DOI] [Google Scholar]
- 137.Ingraham JB, Baranov M, Costello Z, Barber KW, Wang W, Ismail A, Frappier V, Lord DM, Ng-Thow-Hing C, Van Vlack ER, Tie S, Xue V, Cowles SC, Leung A, Rodrigues JV, Morales-Perez CL, Ayoub AM, Green R, Puentes K, Oplinger F, Panwar NV, Obermeyer F, Root AR, Beam AL, Poelwijk FJ and Grigoryan G, Illuminating protein space with a programmable generative model, Nature, 2023, 623(7989), 1070–1078, DOI: 10.1038/s41586-023-06728-8, (accessed August 22, 2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.Qayyum N, Seo H, Khan N, Manan A, Ramachandran R, Haseeb M, Kim E and Choi S, Int. J. Biol. Macromol, 2025, 316, 144652. [DOI] [PubMed] [Google Scholar]
- 139.Chang L, Mondal A, Singh B, Martínez-Noa Y and Perez A, Wiley Interdiscip. Rev.: Comput. Mol. Sci, 2024, 14, e1693. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Torres MDT, Chen T, Wan F, Chatterjee P and de la Fuente-Nunez C , bioRxiv, 2025, preprint, DOI: 10.1101/2025.01.31.636003. [DOI] [Google Scholar]
- 141.Chen R, You Y, Liu Y, Sun X, Ma T, Lao X and Zheng H, Microb. Biotechnol, 2025, 18, e70121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 142.Wang J, Feng J, Kang Y, Pan P, Ge J, Wang Y, Wang M, Wu Z, Zhang X, Yu J, Zhang X, Wang T, Wen L, Yan G, Deng Y, Shi H, Hsieh C-Y, Jiang Z and Hou T, Sci. Adv, 2025, 11, eads8932. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 143.Mesa A, Orrego A, Branch-Bedoya JW, Mera-Banguero C and Orduz S, Curr. Microbiol, 2025, 82, 379. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 144.Chen S, Lin T, Basu R, Ritchey J, Wang S, Luo Y, Li X, Pei D, Kara LB and Cheng X, Nat. Commun, 2024, 15, 1611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 145.Zhao W, Hou K, Shen Y and Hu X, Bioinformatics, 2025, 41, btaf069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 146.Wang Y, Song M, Liu F, Liang Z, Hong R, Dong Y, Luan H, Fu X, Yuan W, Fang W, Li G, Lou H and Chang W, Sci. Adv, 2025, 11, eadp7171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Das P, Sercu T, Wadhawan K, Padhi I, Gehrmann S, Cipcigan F, Chenthamarakshan V, Strobelt H, dos Santos C, Chen P-Y, Yang YY, Tan JPK, Hedrick J, Crain J and Mojsilovic A, Nat. Biomed. Eng, 2021, 5, 613–623. [DOI] [PubMed] [Google Scholar]
- 148.Yu Q, Zhang Z, Liu G, Li W and Tang Y, Briefings Bioinf, 2024, 25, bbae583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 149.Ebrahimikondori H, Sutherland D, Yanai A, Richter A, Salehi A, Li C, Coombe L, Kotkoff M, Warren RL and Birol I, Protein Sci., 2024, 33, e5076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Yu C, Fang X, Tian S and Liu H, Nat. Mach. Intell, 2025, 7, 278–292. [Google Scholar]
- 151.Li Y, Wu M, Fu Y, Xue J, Yuan F, Qu T, Rissanou AN, Wang Y, Li X and Hu H, Pharmacol. Res, 2024, 203, 107137. [DOI] [PubMed] [Google Scholar]
- 152.Wang Z, Wu J, Zheng M, Geng C, Zhen B, Zhang W, Wu H, Xu Z, Xu G, Chen S and Li X, J. Chem. Inf. Model, 2024, 64, 9361–9373. [DOI] [PubMed] [Google Scholar]
- 153.Chen R, You Y, Liu Y, Sun X, Ma T, Lao X and Zheng H, Microb. Biotechnol, 2025, 18, e70121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Cabas-Mora G, Daza A, Soto-García N, Garrido V, Alvarez D, Navarrete M, Sarmiento-Varón L, Sepúlveda Yañez JH, Davari MD, Cadet F, Olivera-Nappa Á, Uribe-Paredes R and Medina-Ortiz D, Database, 2024, 2024, baae113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Tan JZE, Wee J, Gong X and Xia K, J. Chem. Inf. Model, 2025, 65, 4232–4242. [DOI] [PubMed] [Google Scholar]
- 156.Dahl D, Onishi Y, Norwood P, Huh R, Bray R, Patel H and Rodríguez Á, JAMA, 2022, 327, 534–545. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157.Aronne LJ, Sattar N, Horn DB, Bays HE, Wharton S, Lin W-Y, Ahmad NN, Zhang S, Liao R, Bunck MC, Jouravskaya I and Murphy MA, and SURMOUNT-4 Investigators, JAMA, 2024, 331, 38–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Wharton S, le Roux CW, Kosiborod MN, Platz E, Brueckmann M, Jastreboff AM, Ajaz Hussain S, Pedersen SD, Borowska L, Unseld A, Kloer IM and Kaplan LM, Obesity, 2025, 33, 67–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 159.Jeppesen PB, Vanuytsel T, Subramanian S, Joly F, Wanten G, Lamprecht G, Kunecki M, Rahman F, Nielsen TSS, Berner-Hansen M, Pape U-F and Mercer DF, Gastroenterology, 2025, 168, 701–713. [DOI] [PubMed] [Google Scholar]
- 160.Hennrich U and Eder M, Pharmaceuticals, 2022, 15, 1292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 161.Solis-Herrera C, Kane MP and Triplitt C, Clin. Diabetes, 2024, 42, 74–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 162.Guerlavais V, Sawyer TK, Carvajal L, Chang YS, Graves B, Ren J-G, Sutton D, Olson KA, Packman K, Darlak K, Elkin C, Feyfant E, Kesavan K, Gangurde P, Vassilev LT, Nash HM, Vukovic V, Aivado M and Annis DA, J. Med. Chem, 2023, 66, 9401–9417. [DOI] [PubMed] [Google Scholar]
- 163.Kim S, No YH, Sluyter R, Konstantinov K, Kim YH and Kim JH, Coord. Chem. Rev, 2024, 500, 215530. [Google Scholar]
- 164.Wang D, Yin F, Li Z, Zhang Y and Shi C, J. Nanobiotechnol, 2025, 23, 305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 165.Nissan N, Allen MC, Sabatino D and Biggar KK, Biomolecules, 2024, 14, 1303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 166.Bayley O, Savino E, Slattery A and Noël T, Matter, 2024, 7, 2382–2398. [Google Scholar]
- 167.Talluri YN, Sankaranarayanan SK, Fry HC and Batra R, Sci. Adv, 2025, 11, eadt9466. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 168.Kazmirchuk TDD, Bradbury-Jost C, Withey TA, Gessese T, Azad T, Samanfar B, Dehne F and Golshani A, Genes, 2023, 14, 1194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 169.Gładysz MZ, Stevanoska M, Włodarczyk-Biegun MK and Nagelkerke A, Adv. Drug Delivery Rev, 2022, 184, 114183. [DOI] [PubMed] [Google Scholar]
- 170.Yang R, Ma X, Peng F, Wen J, Allahou LW, Williams GR, Knowles JC and Poma A, Biotechnol. Adv, 2025, 81, 108570. [DOI] [PubMed] [Google Scholar]
- 171.Xie L, Xie L and Bourne PE, Curr. Opin. Struct. Biol, 2011, 21, 189–199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 172.Mondal A, Singh B, Felkner RH, Falco AD, Swapna G, Montelione GT, Roth MJ and Perez A, Angew. Chem., Int. Ed, 2024, 63, e202405767. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 173.Pei J, Gao X, Pan D, Hua Y, He J, Liu Z and Dang Y, Curr. Res. Food Sci, 2022, 5, 2162–2170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 174.Tan X, Liu Q, Fang Y, Yang S, Chen F, Wang J, Ouyang D, Dong J and Zeng W, Briefings Bioinf., 2024, 25, bbae350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 175.Achilleos K, Petrou C, Nicolaidou V and Sarigiannis Y, J. Pept. Sci, 2025, 31, e70016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 176.Seo SY and Rhee J-K, Bioinformatics, 2025, 41, i125–i132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 177.Puig M and Shubow S, Front. Immunol, 2025, 16, 1608401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 178.Ha Gan B, Gaynord J, Rowe SM, Deingruber T and Spring DR, Chem. Soc. Rev, 2021, 50, 7820–7880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 179.Starr CG, He J and Wimley WC, ACS Chem. Biol, 2016, 11, 3391–3399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 180.Ghaly G, Tallima H, Dabbish E, Badr ElDin N, Abd El-Rahman MK, Ibrahim MAA and Shoeib T, Molecules, 2023, 28, 1148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 181.Hashemi S, Vosough P, Taghizadeh S and Savardashtaki A, Heliyon, 2024, 10(22), e40265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 182.Venkataraman M, Rao GC, Madavareddi JK and Maddi SR, ADMET DMPK, 2025, 13, 2772. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 183.Alzubaidi L, Bai J, Al-Sabaawi A, Santamaría J, Albahri AS, Al-dabbagh BSN, Fadhel MA, Manoufali M, Zhang J, Al-Timemy AH, Duan Y, Abdullah A, Farhan L, Lu Y, Gupta A, Albu F, Abbosh A and Gu Y, J. Big Data, 2023, 10, 46. [Google Scholar]
- 184.Wan F, Wong F, Collins JJ and de la Fuente-Nunez C, Nat. Rev. Bioeng, 2024, 2, 392–407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 185.Sidorczuk K, Gagat P, Pietluch F, Kała J, Rafacz D, Bąkała L, Słowik J, Kolenda R, Rödiger S, Fingerhut LCHW , Cooke IR, Mackiewicz P and Burdukiewicz M, Briefings Bioinf., 2022, 23, bbac343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 186.Brizuela CA, Liu G, Stokes JM and de la Fuente-Nunez C, Microb. Biotechnol, 2025, 18, e70072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 187.Bornmann L, Haunschild R and Mutz R, Humanit. Soc. Sci. Commun, 2021, 8, 224. [Google Scholar]
- 188.Deshpande D, Chhugani K, Ramesh T, Pellegrini M, Shiffman S, Abedalthagafi MS, Alqahtani S, Ye J, Shirley Liu X, Leek JT, Brazma A, Ophoff RA, Rao G, Butte AJ, Moore JH, Katritch V and Mangul S, Cell, 2024, 187, 4449–4457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 189.Joly Y, Dyke SOM, Knoppers BM and Pastinen T, Cell, 2016, 167, 1150–1154. [DOI] [PubMed] [Google Scholar]
- 190.Li H, Lv L, Cao H, Liu Z, Yan Z, Wang Y, Tian Y, Li Y and Yuan L, arXiv, 2025, preprint, arXiv:2504.12314, DOI: 10.48550/arXiv.2504.12314. [DOI] [Google Scholar]
- 191.Abbaszadeh A and Shahlaee A, arXiv, 2025, preprint, arXiv: 2508.18446, DOI: 10.48550/arXiv.2508.18446. [DOI] [Google Scholar]
- 192.Wittmann BJ, Alexanian T, Bartling C, Beal J, Clore A, Diggans J, Flyangolts K, Gemler BT, Mitchell T, Murphy ST, Wheeler NE and Horvitz E, Science, 2025, 390, 82–87. [DOI] [PubMed] [Google Scholar]
- 193.Nisa U, Shirazi M, Saip MA and Pozi MSM, J. Autom. Intell, 2025, DOI: 10.1016/j.jai.2025.08.003. [DOI] [Google Scholar]
- 194.Hosseini S and Seilani H, Array, 2025, 26, 100399. [Google Scholar]
- 195.Ghafarollahi A and Buehler MJ, Digital Discovery, 2024, 3, 1389–1409. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 196.Ghafarollahi A and Buehler MJ, arXiv, 2025, preprint, arXiv: 2504.19017, DOI: 10.48550/arXiv.2504.19017. [DOI] [Google Scholar]
- 197.Ünlü A, Rohr P and Celebi A, arXiv, 2025, preprint, arXiv: 2508.03444, DOI: 10.48550/arXiv.2508.03444. [DOI] [Google Scholar]
- 198.Rudin C, Nat. Mach. Intell, 2019, 1, 206–215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 199.Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q and Zhou D, arXiv, 2023, preprint, arXiv:2201.11903, DOI: 10.48550/arXiv.2201.11903. [DOI] [Google Scholar]
- 200.Wang R, Zhang H, Nguyen T, Feng S, Pang H-W, Yu X, Xiao L and Zhang PZ, arXiv, 2025, preprint, arXiv:2508.14765, DOI: 10.48550/arXiv.2508.14765. [DOI] [Google Scholar]
- 201.Narayanan SM, Braza JD, Griffiths R-R, Bou A, Wellawatte G, Ramos MC, Mitchener L, Rodriques SG and White AD, arXiv, 2025, preprint, arXiv:2506.17238, DOI: 10.48550/arXiv.2506.17238. [DOI] [Google Scholar]
- 202.Buehler MJ, npj Artif. Intell, 2025, 1, 4. [Google Scholar]
- 203.Sha CM, Wang J and Dokholyan NV, Briefings Bioinf., 2024, 25, bbad456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 204.Unke OT, Chmiela S, Sauceda HE, Gastegger M, Poltavsky I, Schütt KT, Tkatchenko A and Müller K-R, Chem. Rev, 2021, 121, 10142–10186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 205.Pun GPP, Batra R, Ramprasad R and Mishin Y, Nat. Commun, 2019, 10, 2339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 206.Venkatraman A, Wilson MA and de Oca Zapiain D. Montes , npj Comput. Mater, 2025, 11, 24. [Google Scholar]
- 207.Boudi AL, Boudi M, Chan C and Boudi FB, Cureus, 2024, 16(11), e74495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 208.Bloomfield D, Pannu J, Zhu AW, Ng MY, Lewis A, Bendavid E, Asch SM, Hernandez-Boussard T, Cicero A and Inglesby T, Science, 2024, 385, 831–833. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 209.Zhai S, Liu T, Lin S, Li D, Liu H, Yao X and Hou T, Drug Discovery Today, 2025, 30, 104300. [DOI] [PubMed] [Google Scholar]
- 210.de Raffele D and Ilie IM, Chem. Commun, 2024, 60, 632–645. [DOI] [PubMed] [Google Scholar]
- 211.Yang S, Ren J, Gao W, Cao L and Ling S, npj Soft Matter, 2025, 1, 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No primary research results, software, or code have been included, and no new data were generated or analyzed as part of this review.