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Artificial Intelligence in Health New drug discovery in the AI era
Figure 4. A brief history of artificial intelligence-driven drug discovery beginning 2017 – 2018
Figure 5. Artificial intelligence, its subsets, and respective tools
Next-generation AI/ML tools, such as AIDDISON, and geographical ethnicity differences in patient
PREDICT, MANTRA, RoseTTAfold, ESMFold, population response (driven by physiological, genetic,
OpenFold, ProGen, ProteinMPNN, EvoDiff, RFdiffusion, and environmental factors) to administered NCEs. 24,35,36
BioGPT, chatPandaGPT, enhance data quality and Molecular docking tools, such as AutoDock 4, AutoDock
prediction accuracy by integrating PK profiles, DDI, Vina, DiffDock, Deep Docking, and DL-DockVS, dock
off-target toxicity, chemical scaffold-driven toxicity, a single ligand by evaluating different poses and atoms
animal toxicity versus human primary culture toxicity, in parallel, reducing computational analysis time. 37,38
Volume 2 Issue 2 (2025) 34 doi: 10.36922/aih.4423
