Table 3. Additional Successes in NP Drug Discovery Achieved Through AI.

Description	AI Application	Results	refs
Development and validation of P-SAMPNN neural network for antiosteoclastogenesis, screening NPs, and drug discovery.	Screening NPs and drug discovery.	Identified 5 confirmed hits among 10 virtual hits; two compounds were potent nanomolar inhibitors.	Liu et al.91
Screening of 150,000 molecules from NP libraries for anticancer activity using ML.	Screening NPs, filtering drug-like molecules, evaluating anticancer activity.	Identified three potential inhibitors confirmed by MD simulations.	Agarwal et al.92
Discovery of abaucin (Figure 8), a narrow-spectrum antibiotic, against Acinetobacter baumannii using ML.	Exploring chemical options against antibiotic-resistant bacteria.	Abaucin targets A. baumannii by disrupting lipoprotein transport via LolE.	Liu et al.93
VS using ML to find mimetics of (−)-galantamine for Alzheimer’s disease.	Multitarget drug design.	Discovered eight compounds with polypharmacological effects.	Grisoni et al.94
Prediction of antibacterial compounds from a vast compound library using a deep neural network.	Discovering new antibiotics.	Discovered halicin as a potent broad-spectrum bactericidal antibiotic.	Stokes et al.95
Enhanced predictor for nonribosomal peptide synthetase (NRPS) adenylation domain specificity using SVM.	Discovering new gene clusters.	Achieved high F-measures for broader and detailed levels of specificity.	Röttig et al.96
MS2Mol: A de novo structure prediction model for identifying small molecules using MS.	Advancing drug discovery.	Predicted 21% of structures with close-match accuracy.	Butler et al.97
DL model for predicting indications and identifying privileged scaffolds in NPs.	Identifying privileged scaffolds for drug design.	Formed a Privileged Scaffold Data set (PSD) for lead compounds.	Lai et al.98
Identification of troxerutin (Figure 8) as a TRPV1 antagonist using MT-DTI model.	Identifying potential compounds for specific biological targets.	Troxerutin showed efficacy in reducing skin redness in clinical trials.	Lee et al.99
Discovery of sclareol (Figure 8) as a Cav1.3 antagonist for Parkinson’s disease using a drug-discovery platform.	Identifying potential compounds for specific diseases.	Sclareol reduced motor deficits in a Parkinson disease mouse model.	Wang et al.100
OptNCMiner model for predicting multitarget modulating NPs.	Understanding biological activity.	Identified compounds for type 2 diabetes mellitus complications.	Shin et al.17
ML method for identifying NPs and visualizing key atoms.	Quantifying NP-likeness.	Achieved high accuracy with AUC of 0.997 and MCCs above 0.954.	Chen et al.101
NIMO: a molecular generative model for expanding chemical diversity of NPs.	Enhancing chemical diversity.	Excelled in generating molecules from scratch and optimizing structures.	Shen et al.102
Andrographolide (Figure 8) identified as an anti-Trypanosoma cruzi compound using ML.	Predicting activity of plant-based NPs against Chagas disease.	Exhibited significant anti-T. cruzi activity with low cytotoxicity.	Barbosa et al.103
Designing new small molecules targeting SARS-CoV-2 protease using generative and predictive models.	Targeting SARS-CoV-2 protease.	Identified 31 potential New Chemical Entities (NCE), some like HIV protease inhibitors.	Bung et al.104
AI-driven discovery of functional ingredient NRT_N0G5IJ for glucose regulation from Pisum sativum.	Supporting glucose regulation.	Reduced HbA1c and fasting glucose levels in human trials.	Chauhan et al.105