Artificial Intelligence in Drug Discovery & Development

Drug discovery and development is a complex, costly, and time-intensive process involving target identification, optimization, preclinical evaluation, clinical trials, regulatory approval, and post-market monitoring. Advances in artificial intelligence (AI) are transforming this landscape by enabling data-driven decision-making, reducing timelines, and improving predictive accuracy across the pharmaceutical pipeline. AI supports target identification through genomic and proteomic data analysis, enables de novo drug design with generative models, and aids property prediction and toxicity study using machine learning. In preclinical Trails, AI improves pharmacokinetic and pharmacodynamic modeling, predicts ADMET parameters, and supports the 3Rs principle by reducing animal use. Clinical research benefits from AI-driven patient recruitment, adaptive trial design, adherence tracking, and decentralized execution. Manufacturing and formulation are optimized with AI-based modeling, automation, and quality control. Regulatory review increasingly integrates AI for dossier evaluation, real-world evidence analysis, and safety monitoring, while post-marketing surveillance applies NLP and machine learning to detect adverse drug reactions and safety signals from diverse data sources. Despite challenges in data quality, interpretability, ethics, and regulatory acceptance, AI promises to reduce costs, improve efficiency, accelerate innovation in drug discovery and development. Future progress requires explainable, transparent, & globally harmonized AI solutions.

Artificial intelligence, Drug discovery, Machine learning, Pharmacovigilance, ADMET prediction, Clinical trials

1. Ocana A, Pandiella A, Privat C, Bravo I, Luengo-Oroz M, Amir E, Gyorffy B. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research. 2025 Mar 14;13(1):45. [Google Scholar] [Crossref]

2. Chen Y, Yang M, Hua Q. Artificial intelligence in central-peripheral interaction organ crosstalk: The future of drug discovery and clinical trials. Pharmacological Research. 2025 Apr 10:107734. [Google Scholar] [Crossref]

3. McGinnity DF, Meneyrol J, Boldron C, Johnstone C. Every Compound a Candidate: experience-led risk-taking approaches to accelerate small-molecule drug discovery. Drug Discovery Today. 2025 Apr 8:104354. [Google Scholar] [Crossref]

4. Kokudeva M, Vichev M, Naseva E, Miteva DG, Velikova T. Artificial intelligence as a tool in drug discovery and development. World Journal of Experimental Medicine. 2024 Sep 20;14(3):96042. [Google Scholar] [Crossref]

5. Harvey A. The role of natural products in drug discovery and development in the new millennium. I Drugs. 2010; 13:70–72. [PubMed] [Google Scholar] [Google Scholar] [Crossref]

6. Sivakumar B, Kaliappan I. Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors. PHAR. 2023; 70:1529–1540. [Google Scholar] [Google Scholar] [Crossref]

7. Kumar V, Roy K. Embracing the changes and challenges with modern early drug discovery. Expert Opinion on Drug Discovery. 2025 Apr 3;20(4):419-31. [Google Scholar] [Crossref]

8. Garg P, Singhal G, Kulkarni P, Horne D, Salgia R, Singhal SS. Artificial intelligence–driven computational approaches in the development of anticancer drugs. Cancers. 2024 Nov 20;16(22):3884. [Google Scholar] [Crossref]

9. Varg P, Singhal G, Kulkarni P, Horne D, Salgia R, Singhal SS. Artificial intelligence–driven computational approaches in the development of anticancer drugs. Cancers. 2024 Nov 20;16(22):3884 [Google Scholar] [Crossref]

10. Garg P, Singhal G, Kulkarni P, Horne D, Salgia R, Singhal SS. Artificial intelligence–driven computational approaches in the development of anticancer drugs. Cancers. 2024 Nov 20;16(22):3884. [Google Scholar] [Crossref]

11. Jarallah SJ, Almughem FA, Alhumaid NK, Fayez NA, Alradwan I, Alsulami KA, Tawfik EA, Alshehri AA. Artificial intelligence revolution in drug discovery: A paradigm shifts in pharmaceutical innovation. International Journal of Pharmaceutics. 2025 May 30:125789. [Google Scholar] [Crossref]

12. Rotake SB, Hatwar PR, Bakal RL, Meshram SI. The role of Artificial Intelligence in drug discovery. Journal of Drug Delivery & Therapeutics. 2025 Jul 1;15(7). [Google Scholar] [Crossref]

13. Patel V, Shah M. Artificial intelligence and machine learning in drug discovery and development. Intelligent Medicine. 2022 Aug 1;2(3):134-40. [Google Scholar] [Crossref]

14. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug discovery today. 2019 Mar 1;24(3):773-80. [Google Scholar] [Crossref]

15. Agrawal P. Artificial intelligence in drug discovery and development. Journal of Pharmacovigilance. 2018;6(2):1000e173. [Google Scholar] [Crossref]

16. Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular therapy Nucleic acids. 2023 Mar 14; 31:691-702. [Google Scholar] [Crossref]

17. Ocana A, Pandiella A, Privat C, Bravo I, Luengo-Oroz M, Amir E, Gyorffy B. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research. 2025 Mar 14;13(1):45. [Google Scholar] [Crossref]

18. Bassey GE, Daniel EA, Okesina KB, Odetayo AF. Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects. Drug Design, Development and Therapy. 2025 Dec 31:7493-502. [Google Scholar] [Crossref]

19. Sulahian R, et al. Synthetic lethal interaction of SHOC2 depletion with MEK Inhibition in RAS-Driven cancers. Cell Rep. 2019; 29:118–e1348. [Google Scholar] [Crossref]

20. Kryukov GV, et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science. 2016; 351:1214–8. [Google Scholar] [Crossref]

21. Imming P, Sinning C, Meyer A. Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov. 2006; 5:821–34. [Google Scholar] [Crossref]

22. Ocana A, Pandiella A, Privat C, Bravo I, Luengo-Oroz M, Amir E, Gyorffy B. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research. 2025 Mar 14;13(1):45. [Google Scholar] [Crossref]

23. Akhtar M, Nehal N, Gull A, Parveen R, Khan S, Khan S, Ali J. Explicating the transformative role of artificial intelligence in designing targeted nanomedicine. Expert Opinion on Drug Delivery. 2025 May 23:1-21. [Google Scholar] [Crossref]

24. Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR–Cas9. Nat Rev Genet. 2015;16:299–311. [Google Scholar] [Crossref]

25. Wang T, et al. Identification and characterization of essential genes in the human genome. Science. 2015; 350:1096–101. [Google Scholar] [Crossref]

26. Rask-Andersen M, Almén MS, Schiöth HB. Trends in the exploitation of novel drug targets. Nat Rev Drug Discov. 2011; 10:579–90. [Google Scholar] [Crossref]

27. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug discovery today. 2019 Mar 1;24(3):773-80. [Google Scholar] [Crossref]

28. Sankar R, Alam MN. Artificial Intelligence in Drug Development and Delivery: Opportunities, Challenges, and Future Directions. Journal of Angiotherapy. 2024 Aug 9;8(8):1-0. [Google Scholar] [Crossref]

29. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, Al Muhanna D, Al-Muhanna FA. A review of the role of artificial intelligence in healthcare. Journal of personalized medicine. 2023 Jun 5;13(6):951. [Google Scholar] [Crossref]

30. Vora LK, Gholap AD, Jetha K, Thakur RR, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023 Jul 10;15(7):1916. [Google Scholar] [Crossref]

31. Blanco-Gonzalez A, Cabezon A, Seco-Gonzalez A, Conde-Torres D, Antelo-Riveiro P, Pineiro A, Garcia-Fandino R. The role of AI in drug discovery: challenges, opportunities, and strategies. Pharmaceuticals. 2023 Jun 18;16(6):891. [Google Scholar] [Crossref]

32. Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences. 2023 Jan 19;24(3):2026. [Google Scholar] [Crossref]

33. Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences. 2023 Jan 19;24(3):2026. [Google Scholar] [Crossref]

34. Xiao, X.; Min, J.L.; Lin, W.Z.; Liu, Z.; Cheng, X.; Chou, K.C. I Drug-Target: Predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J. Biomol. Struct. Dyn. 2015, 33, 2221–2233. [Google Scholar] [CrossRef] [Google Scholar] [Crossref]

35. Koromina, M.; Pandi, M.T.; Patrinos, G.P. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. Omics 2019, 23, 539–548. [Google Scholar] [CrossRef] [Google Scholar] [Crossref]

36. Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent. Sci. 2017, 4, 120–131. [Google Scholar] [CrossRef] [Google Scholar] [Crossref]

37. Kim, K.H.; Kim, N.D.; Seong, B.L. Pharmacophore-based virtual screening: A review of recent applications. Expert Opin. Drug Discov. 2010, 5, 205–222. [Google Scholar] [CrossRef] [Google Scholar] [Crossref]

38. Serrano DR, Luciano FC, Anaya BJ, Ongoren B, Kara A, Molina G, Ramirez BI, Sánchez-Guirales SA, Simon JA, Tomietto G, Rapti C. Artificial intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine. Pharmaceutics. 2024 Oct 14;16(10):1328. [Google Scholar] [Crossref]

39. Obaido G, Mienye ID, Egbelowo OF, Emmanuel ID, Ogunleye A, Ogbuokiri B, Mienye P, Aruleba K. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects. Machine Learning with Applications. 2024 Sep 1; 17:100576. [Google Scholar] [Crossref]

40. Xiao W, Jiang W, Chen Z, Huang Y, Mao J, Zheng W, Hu Y, Shi J. Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines. Signal Transduction and Targeted Therapy. 2025 Mar 5;10(1):74. [Google Scholar] [Crossref]