Artificial Intelligence in Pharmacology, Drug Safety and Toxicity

Artificial intelligence (AI) is transforming pharmacology, drug safety, and toxicology by accelerating the drug development process to be more efficient, precise, and economical. Conventional drug discovery, pre-clinical testing, and post-marketing surveillance methods frequently encounter high costs, long lead times, ethical constraints, and low predictive validity in human outcomes. Utilizing machine learning (ML) and deep learning (DL), AI combines heterogenous datasets chemical structures, genomics, clinical data, and imaging to bridge these gaps.In drug design and discovery, AI has hastened predictions of protein and RNA structures (e.g., AlphaFold), enhanced virtual screening, and enabled de novo drug design with generative models. It has also hastened peptide-based drug development and improved pharmacokinetic prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) and reduced failure rates.

Artificial intelligence, Pharmacology, Drug discovery, Compound Pharmacokinetic Prediction, Clinical Pharmacology, Toxicity

1. Li W, Tan M, Lao L, Wang H, Zheng X, Zhang Y, et al. A comprehensive review of artificial intelligence for pharmacology research. Front Genet. 2024;15:1450529. [Google Scholar] [Crossref]

2. Akhtar A. The flaws and human harms of animal experimentation. Camb Q Healthc Ethics. 2015;24(4):407–19. [Google Scholar] [Crossref]

3. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–50. [Google Scholar] [Crossref]

4. Johnson M, Patel M, Phipps A, van der Schaar M, Boulton D, Gibbs M. The potential and pitfalls of artificial intelligence in clinical pharmacology. CPT Pharmacometrics Syst Pharmacol. 2023;12(3):279–84. [Google Scholar] [Crossref]

5. Li R, Zhou D, Shen A, Zhang A, Su M, Li M, et al. Physical formula enhanced multi-task learning for pharmacokinetics prediction. arXiv [preprint]. 2024 Apr 16. [Google Scholar] [Crossref]

6. Boelsterli UA. Animal models of human disease in drug safety assessment. J Toxicol Sci. 2003;28(3):109–21. [Google Scholar] [Crossref]

7. Chen M, Suzuki A, Thakkar S, Yu K, Hu C, Tong W, et al. Idiosyncratic drug hepatotoxicitu: strategy for prevention and proposed mechanism. Drug Discov Today. 2013;18(15-16): 867-873.7 [Google Scholar] [Crossref]

8. Li L, Zhang W, Yang L. Leveraging network pharmacology for drug discovery. Trends Pharmacol Sci. 2025;46(5):345–58. [Google Scholar] [Crossref]

9. Zhai Y, Li L. Network pharmacology: a crucial approach in traditional Chinese medicine. Chin Med. 2025;15:1. doi:10.1186/s13020-024-01056-z. [Google Scholar] [Crossref]

10. Noor F, Ali M, Rehman A, et al. Network pharmacology approach for medicinal plants. Front Pharmacol. 2022;13:9143318. doi:10.3389/fphar.2022.9143318. [Google Scholar] [Crossref]

11. Ozsgai K, et al. Analysis of pharmacovigilance databases for spontaneous adverse drug reactions. Pharmacoepidemiol Drug Saf. 2022;31(10):1234–41. [Google Scholar] [Crossref]

12. Blanco-González A, et al. The role of AI in drug discovery: challenges and opportunities. Nat Rev Drug Discov. 2023;22(3):199–215. [Google Scholar] [Crossref]

13. Floudas CA, Fung HK, McAllister SR, Mönnigmann M, Rajgaria R. Advances in protein structure prediction and de novo protein design. Chem Eng Sci. 2006;61(3):966–88. [Google Scholar] [Crossref]

14. Rohl CA, Strauss CEM, Misura KM, Baker D. Protein structure prediction using Rosetta. Methods Enzymol. 2004;383:66–93. [Google Scholar] [Crossref]

15. Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, et al. Highly accurate protein structure prediction for the human proteome. Nature. 2021;596(7873):590–6. [Google Scholar] [Crossref]

16. Fisher CK, Stultz CM. Constructing ensembles for intrinsically disordered proteins. J Chem Phys. 2011;135(19):194104. [Google Scholar] [Crossref]

17. Gomes PSFC, et al. Protein structure prediction in the era of AI: Challenges and opportunities. Front Bioinform. 2022;3:983306. [Google Scholar] [Crossref]

18. Nussinov R, et al. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Trends Pharmacol Sci. 2023;44(6):441–53. [Google Scholar] [Crossref]

19. Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–8. [Google Scholar] [Crossref]

20. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016;47:20–33. [Google Scholar] [Crossref]

21. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26:80. [Google Scholar] [Crossref]

22. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev. 2019;119:10520–94. [Google Scholar] [Crossref]

23. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25:1315–60. [Google Scholar] [Crossref]

24. Dobchev D, Karelson M. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework? Expert Opin Drug Deliv. 2016;11:627–39. [Google Scholar] [Crossref]

25. Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm. 2017;14:4462–75. [Google Scholar] [Crossref]

26. Lin E, Lin C-H, Lane H-Y. Relevant applications of generative adversarial networks in drug design and discovery: molecular de novo design, dimensionality reduction, and de novo peptide and protein design. Molecules. 2020;25:3250. [Google Scholar] [Crossref]

27. Kim C, Zhu V, Obeid J, Lenert L. Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke. PLoS One. 2019;14:e0212778. [Google Scholar] [Crossref]

28. Han J, et al. PepNet: An interpretable neural network for antimicrobial and anti-inflammatory peptide prediction. Commun Biol. 2024;7(1):1–12. [Google Scholar] [Crossref]

29. Kabra R, Ghosh S, Ghosh S, et al. Evolutionary artificial intelligence based peptide library design against SARS-CoV-2 main protease. Comput Biol Med. 2021;137:104788. [Google Scholar] [Crossref]

30. Rein D, Ternes P, Demin R, et al. Artificial intelligence identified peptides modulate inflammation in healthy adults. Food Funct. 2019;10(12):7692–700. doi:10.1039/c9fo01398a. [Google Scholar] [Crossref]

31. Liu Y, Li Y, Wang H, et al. Accelerating ligand-based virtual screening with AI and machine learning. Drug Discov Today. 2024;29(2):123–33. [Google Scholar] [Crossref]

32. Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. 2019;20:1878–912. [Google Scholar] [Crossref]

33. Morris GM, Lim-Wilby M. Molecular docking. In: Molecular modeling. Totowa (NJ): Humana Press; 2008. p. 365–82. [Google Scholar] [Crossref]

34. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61. [Google Scholar] [Crossref]

35. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47:1739–49. [Google Scholar] [Crossref]

36. Ewing TJ, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des. 2001;15:411–28. [Google Scholar] [Crossref]

37. Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019;20:4331. [Google Scholar] [Crossref]

38. Kadioglu O, Efferth T. A machine learning-based prediction platform for P-glycoprotein modulators and its validation by molecular docking. Cells. 2019;8:1286. [Google Scholar] [Crossref]

39. Chandak T, Mayginnes JP, Mayes H, Wong CF. Using machine learning to improve ensemble docking for drug discovery. Proteins. 2020;88:1263–70. [Google Scholar] [Crossref]

40. Maia EHB, Assis LC, De Oliveira TA, Da Silva AM, Taranto AG. Structure-based virtual screening: from classical to artificial intelligence. Front Chem. 2020;8:343. [Google Scholar] [Crossref]

41. Yasuo N, Sekijima M. Improved method of structure-based virtual screening via interaction-energy-based learning. J Chem Inf Model. 2019;59:1050–61. [Google Scholar] [Crossref]

42. Ballester PJ, Mitchell JB. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics. 2010;26:1169–75. [Google Scholar] [Crossref]