Table 3.
Examples of Recent Applications of AI/ML in Drug Discovery Applications
| Adverse event detection Identify adverse events associated with drugs by analyzing electronic health records, social media, and other real-world data sources. This can assist in early detection, reporting, and mitigating potential drug risks.58 |
| Antibiotic discovery Antibiotic resistance is a major public health concern, and there is a need for new antibiotics. Help discover new antibiotics by analyzing vast amounts of chemical and biological data to identify potential antimicrobial compounds.59 |
| Autoimmune diseases Aid in developing drugs for autoimmune diseases by predicting disease mechanisms, identifying potential therapeutic targets, and optimizing treatment strategies.60 |
| Biomarker discovery Analyze large-scale omics data, such as genomics, proteomics, and metabolomics, to identify potential biomarkers for disease diagnosis, prognosis, and treatment response. This can aid in personalized medicine and drug development.61 |
| Biomedical images Analyze biomedical images, such as images from microscopy, to identify potential targets for drug development. This can provide valuable information about the effects of potential drugs on cells and tissues.62 |
| Cancer immunotherapy Development of cancer immunotherapies, such as immune checkpoint inhibitors and CAR-T cell therapies. Machine learning models can predict patient responses, identify potential biomarkers, and optimize treatment protocols.63 |
| Cell therapy Developing cell therapies involves introducing new cells into a patient to treat a disease. Machine learning models can help optimize the production of therapeutic cells and ir effects on patients.64 |
| Clinical trial design optimization Optimize clinical trial design by considering patient characteristics, treatment protocols, and trial outcomes. This can help improve the efficiency and success rate of clinical trials.65 |
| Clinical trials Help improve the efficiency of clinical trials, which are critical in drug development. This can involve predicting the outcomes of trials, optimizing the design of trials, or identifying suitable patients for trials.66 |
| Clinical trials Optimize the planning and execution of clinical trials, the research that examines the security and efficiency of novel drugs in people. The best trial candidates, trial results, and trial data analysis can all be assisted by machine learning algorithms.67 |
| Combination optimization Optimize the selection and dosing of drug combinations to maximize efficacy and minimize side effects. Machine learning models can analyze data on drug interactions, synergistic effects, and patient characteristics to identify the most effective combinations.68 |
| Combination sensitivity prediction Sensitivity of cancer cells to specific drug combinations, aiding in identifying effective treatment regimens for personalized cancer therapy.69 |
| Combination side effect prediction Side effects of drug combinations by analyzing the known side effect profiles of individual drugs and their interactions. This can help identify potential safety concerns and optimize drug combination therapies.70 |
| Continuous manufacturing Optimize continuous manufacturing processes in the pharmaceutical industry by monitoring and controlling key process parameters in real time. This can enhance quality control, reduce waste, and increase efficiency.71 |
| De novo design Creating new and effective drug molecules from scratch is a complex task given the many possible molecules. Help with this by generating and optimizing potential drug molecules for specific targets or diseases.72 |
| Delivery optimization Optimize drug delivery systems by predicting drug release profiles, designing targeted delivery vehicles, and enhancing efficiency.73 |
| Delivery systems Design of drug delivery systems, which control the release and distribution of drugs in the body. Machine learning algorithms can the effectiveness of different drug delivery systems, leading to better treatments with fewer side effects.74 |
| Dermatology Analyze skin images to diagnose skin diseases and predict responses to treatment. This could aid in the development of drugs for dermatological conditions.75 |
| Disease mechanisms Understand the mechanisms of diseases, aiding in the development of new treatments. Analyzing large amounts of biological and medical data reveals novel insights into how diseases develop and progress.76 |
| Disposition modeling Predict drug disposition, including absorption, distribution, metabolism, and excretion (ADME), to understand drug pharmacokinetics and optimize dosing regimens.77 |
| Drug-drug interaction prediction Help discover potential drug-drug interactions and their implications on efficacy and safety, and predict probable drug interactions.78 |
| Environmental Impact of Pharmaceuticals Help assess the environmental impact of pharmaceuticals, a factor that is increasingly considered in drug development. Machine learning models can reduce drugs’ environmental persistence, bioaccumulation, and toxicity, leading to safer and more sustainable treatments.79 |
| Epigenetic discovery Analyze epigenetic data and identify potential epigenetic drug targets that modify gene expression patterns. This can aid in developing therapies for diseases influenced by epigenetic modifications.80 |
| Formulation and Dosage Optimization Assist in optimizing drug formulation and dosage to enhance drug efficacy, minimize side effects, and improve patient compliance. Machine learning models can optimize formulation and dosage based on patient characteristics and drug properties.81 |
| Formulation prediction Based on molecular descriptors and physicochemical characteristics, predict drug formulation properties, such as solubility and stability. This can aid in the development of optimized drug formulations.82 |
| Gene editing Assist in gene editing technologies such as CRISPR-cas9 by predicting off-target effects, guiding the design of gene editing tools, and optimizing editing efficiency.83 |
| Gene therapy Aid in developing gene therapies involves altering the genes within a patient’s cells. Machine learning models can find these alterations’ effects and help design more effective therapies.84 |
| Genome-wide Association Studies (GWAS) Help analyze the results of genome-wide association studies, which involve scanning the genomes of many people to find genetic variations associated with a particular disease. This can uncover new drug targets.85 |
| High-throughput Screening Improve high-throughput screening, a common method for drug discovery, by predicting the properties and potential therapeutic uses of numerous compounds quickly and accurately. Machine learning models can process and learn from vast amounts of chemical and biological data, making them effective tools for drug screening.86 |
| Imaging agents Imaging agents are substances used in medical imaging to highlight certain structures or processes. Design of new imaging agents, which can be used for diagnosis or for tracking the progress of disease or the effect of treatment.87 |
| Immunotherapy Develop new immunotherapies, treatments that use the body’s immune system to fight diseases like cancer. Machine learning models can respond to immunotherapy, identify potential targets, and help design more effective treatments.88 |
| Manufacturing and Quality Control Optimize drug manufacturing processes, predict potential issues, and improve quality control measures. Machine learning models can analyze production data, detect anomalies, and optimize manufacturing parameters to ensure consistent drug quality.89 |
| Metabolism prediction Metabolic fate of drugs in the body, aiding in identifying potential metabolites and facilitating drug development and optimization.90 |
| Metabolomics Analyze metabolomic data, which provides information about the metabolites in a biological sample. This can identify biomarkers for disease, understand the mechanism of action of drugs, and identify potential drug targets.91 |
| Microbiome analysis The microbiome, the collection of microorganisms living in the human body, can affect the effectiveness and toxicity of drugs. Help analyze the complex data from microbiome studies, potentially leading to new strategies for personalized drug therapy.92 |
| Nanomedicine Assist in designing nanoscale drug delivery systems. Machine learning can properties of nanoparticles and their interactions with biological systems, helping to design more effective nano-drugs.93 |
| Natural language processing Analyze scientific literature, patents, and other text sources to extract knowledge and insights for drug discovery and development. Natural language processing techniques can help in data mining, knowledge extraction, and text summarization.94 |
| Natural product discovery Analyze large-scale data on natural products and their biological activities to identify potential drug candidates. This can expedite the discovery of novel compounds with therapeutic potential.95 |
| Neglected diseases Accelerate drug discovery efforts for neglected diseases by predicting potential drug candidates and repurposing existing drugs to treat these conditions.96 |
| Neurodegenerative diseases Discovery of new drugs to prevent diseases like Alzheimer’s and Parkinson’s. Machine learning models can analyze multi-omics data and predict potential therapeutic targets for intervention.97 |
| Neuropharmacology Develop new treatments for neurological and psychiatric disorders. For example, machine learning models can analyze brain images and other data to respond to treatment or to identify new drug targets.98 |
| Ophthalmology AI has also found applications in the development of drugs for eye diseases. For instance, deep learning has been applied to analyze retinal scans, which can provide insights to understand and treat conditions such as age-related macular degeneration.99 |
| Orphan diseases Facilitate drug discovery efforts for orphan diseases by predicting drug-target interactions and repurposing existing drugs for these rare conditions.100 |
| Pain management Aid in the discovery of new drugs for pain management by analyzing large-scale genomic, transcriptomic, and proteomic data to identify potential targets and pathways involved in pain signaling.101 |
| Patient adherence prediction Predict patient adherence to drug regimens by analyzing patient characteristics, social determinants, and past adherence patterns. This can help identify patients at risk of non-adherence and develop interventions to improve drug adherence.102 |
| Patient stratification Assist in patient stratification, which involves grouping patients based on their predicted response to treatment. Identifying patients who can most benefit is a main goal, facilitating the move towards personalized medicine.103 |
| Peptide design Design of peptide drugs, which are a unique class of therapeutic agents. Machine learning algorithms can stabilize potential peptide drugs’ toxicity and activity, facilitating their design.104 |
| Personalized combination therapy Effectiveness of different drug combinations for individual patients based on their genetic and clinical characteristics. This can help in the development of personalized combination therapy for complex diseases.105 |
| Personalized dosing Optimize drug dosing for individual patients by considering age, weight, genetics, and biomarker data. This can help achieve optimal therapeutic outcomes while minimizing adverse effects.106 |
| Personalized medicine By analyzing genetic data, predict individual drug responses and help develop personalized treatment plans. This is particularly relevant in oncology, where genetic variations can significantly affect treatment outcomes.107 |
| Pharmacoepidemiology Be used in pharmacoepidemiology, the study of the uses and effects of drugs in large numbers of people. For example, machine learning models can analyze large health record databases to identify drug use patterns, drug effectiveness in real-world conditions, and the factors influencing these outcomes.108 |
| Pharmacogenomics Analyze genetic variations and drug response data to predict individual drug responses and identify potential adverse reactions, enabling personalized medicine approaches.109 |
| Pharmacokinetic modeling Enhance pharmacokinetic modeling, which involves studying how drugs are absorbed, distributed, metabolized, and excreted by the body. Machine learning models can predict drug concentrations in different tissues and optimize dosing regimens.110 |
| Pharmacokinetics and Pharmacodynamics (PK/PD) Modeling Help develop PK/PD models, which predict how the body will affect the drug (pharmacokinetics) and how the drug will affect the body (pharmacodynamics). Optimal dosing and frequency are one main element of understanding for individual patients.111 |
| Pharmacovigilance signal detection Analyze large-scale pharmacovigilance databases and real-world data to detect potential safety signals and adverse drug reactions. This can enhance the early detection and monitoring of drug safety concerns.112 |
| Interaction of drugs with multiple targets, a field known as polypharmacology. Understanding these interactions can help design drugs to achieve the desired effects and minimize undesired side effects.113 |
| Precision medicine Enable precision medicine by integrating diverse patient data, including genomic information, clinical records, and lifestyle factors. Machine learning models can analyze these data to predict individual treatment responses and tailor therapies for optimal outcomes.114 Best drug combinations for individual cancer patients based on their genetic and clinical data, helping to move toward personalized and precision medicine in oncology.115 |
| Preclinical safety assessment Aid in the preclinical safety assessment of drugs by predicting their potential toxicities and identifying safety risks, leading to more efficient and reliable safety evaluation.116 |
| Predicting interactions Predict potential drug-drug interactions, which are situations where one drug affects the activity of another. This is critical for ensuring patient safety, as drug-drug interactions can lead to adverse effects or reduced treatment efficacy.117 |
| Predicting side effects Potential side effects of drugs contribute to better patient safety and care. Machine learning models can utilize chemical properties, known side effects, and biological data to make these predictions.118 |
| Predictive toxicology Toxicity of chemical compounds and drugs, aiding in the early identification of potential safety issues and reducing the need for animal testing. |
| Pricing and Market Access Analyze healthcare data, market dynamics, and pricing information to optimize drug pricing strategies and improve market access for pharmaceutical companies. This can help ensure the affordability and availability of essential drugs. |
| Proteomics Analyze proteomic data, which provides information about the proteins in a biological sample. This can help to understand the mechanism of action of drugs, identify potential drug targets, and identify biomarkers for disease.119 |
| Quantitative structure-activity relationship (QSAR) models Help develop QSAR models, which show the biological activity of compounds based on their chemical structure. This can accelerate the identification of potential new drugs.120 |
| Rare disease discovery Aid in discovering drugs for rare diseases by analyzing various data sources, including genetic and clinical data, to identify potential therapeutic targets and repurpose existing drugs.121 |
| Regulatory Compliance and Safety Surveillance Aid in regulatory compliance by automating the analysis of safety data, detecting adverse events, and monitoring post-market drug safety. This can improve pharmacovigilance and regulatory decision-making processes.122 |
| Regulatory processes Streamline regulatory processes in drug development, including drug approval and post-marketing surveillance, by automating data analysis, identifying safety signals, and facilitating regulatory decision-making.123 |
| Repositioning for Rare Diseases Identify potential drug candidates for treating rare diseases by analyzing molecular profiles, gene expression data, and clinical information.124 |
| Repositioning, or drug repositioning or repurposing, is finding new uses for already-existing drugs. Predict new therapeutic uses for already-on-The-market drugs by studying data on drug structures, actions, and targets. Repositioning Help identify new uses for existing drugs, a process known as drug repositioning or repurposing. By analyzing data on drug structures, effects, and targets, predict new therapeutic uses for drugs already on the market.125 |
| Reproduction Reproduce discontinued or scarce drugs by analyzing their chemical structures and properties. This can help ensure the continuous availability of essential drugs.126 |
| Repurposing drugs Find new uses for existing drugs, a process known as drug repurposing or repositioning. This can be a quicker and less costly way of developing new treatments than traditional drug development processes.127 |
| Repurposing for Rare Diseases Identify potential drug candidates for treating rare diseases by analyzing drug databases, genomic data, and clinical information. This can expedite the development of therapies for rare conditions.128 |
| Resistance prediction Predict drug resistance in pathogens and cancer cells, aiding in developing strategies to overcome resistance and optimize treatment.129 |
| Response Prediction for Personalized Medicine Predict individual drug responses based on patient-specific factors, such as genetics, clinical data, and environmental factors. This can enable personalized treatment selection and optimization.130 |
| Safety and Adverse Event Prediction Predict and prevent adverse drug events by analyzing data from electronic health records, clinical notes, and other sources. Machine learning models can identify patterns and risk factors associated with adverse events, enabling early detection and prevention.131 |
| Structure-based design Ai/mL models can be utilized in structure-based drug design, which involves drug design based on the drug target’s molecular structure. These models can predict how well potential drugs will bind to the target, helping to identify promising drug candidates.132 |
| Supply chain optimization Optimize the supply chain of pharmaceutical products, including inventory management, demand forecasting, and distribution logistics. This can help improve efficiency, reduce costs, and ensure timely availability of drugs.133 |
| Synergistic combinations Identify combinations of drugs that work synergistically; their combined effect is greater than the sum of their individual effects. This could lead to the development of more effective treatments, particularly for complex diseases like cancer.70 |
| Synthesis planning Assist in planning the synthesis of new drugs by proposing the most effective and feasible routes and steps. This can save time and resources in drug development.134 |
| Synthetic Biology and Biotechnology Optimize the production of bioactive compounds through synthetic biology and biotechnology. For example, machine learning models can optimize the genetic engineering of microorganisms to produce drugs.135 |
| Target binding affinity prediction Small molecule target protein binding affinity facilitates the invention and improvement of therapeutic candidates.136 |
| Target druggability prediction Druggability of potential drug targets, helping to prioritize targets for further investigation and optimizing drug discovery efforts.137 |
| Target identification Identifying potential drug targets by analyzing genomic, proteomic, and other biological data. Machine learning models can uncover associations between targets and diseases, facilitating the discovery of new therapeutic targets.138 |
| Target validation Also assist in target validation, which involves confirming that a biological target (such as a protein or gene) is related to disease and can be acted upon by a drug. Machine learning models can analyze and interpret complex biological data to uncover and validate new drug targets.139 |
| Toxicology predictions Potential drug toxicity is a critical component in drug development. Early detection of adverse effects can reduce the likelihood of expensive failures during the latter phases of drug development.140 |
| Vaccine design Machine learning models can provide immune responses to different vaccine candidates, which can help design more effective vaccines.141 |
| Virtual screening Accelerate the virtual screening process to identify potential drug candidates from large compound libraries. Machine learning models can increase the likelihood of a compound binding to a specific target, aiding drug discovery. 142 |
| Withdrawal prediction Likelihood and severity of withdrawal symptoms when discontinuing certain drugs. This can help healthcare providers develop tapering strategies and support patients in safely discontinuing drugs.143 |