The Role of Artificial Intelligence and Machine Learning in Drug Discovery and Development

PDF Review History

Published: 2024-04-11

Page: 133-140

Bandaru Revanth

Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.

Syed Shuja Asrar

Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.

Binaya Sapkota

Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.

Karnati Vandana

Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.

Kanala Somasekhar Reddy

Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.

Bhupalam Pradeep Kumar *

Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research (RIPER) –Autonomous, Anantapur, Andhra Pradesh, 515721, India.

*Author to whom correspondence should be addressed.


The symbiotic integration of artificial intelligence (AI) and pharmacology marks a paradigm shift in medicine discovery and development. Traditional approaches, formerly constrained by the complications of target identification, high-outturn webbing, and clinical trials, are yielding to the transformative power of AI. This review navigates the elaboration of technology in medicine discovery, from literal limitations to the emergence of AI as a catalyst for effectiveness and perfection.  AI's operations in target identification and high-outturn webbing accelerate processes, furnishing unknown perceptivity to implicit medicine campaigners. In preclinical and clinical development, prophetic modeling for toxin assessment and case position in clinical trials are reshaping the geography, offering a more ethical and individualized approach.  still, this technological advancement isn't without challenges. Data quality, bias in AI models,   interpretability, and nonsupervisory considerations demand careful navigation. Success stories, from AI- AI-designed medicines entering clinical trials to the repurposing of composites, punctuate the palpable impact of this community.  Looking ahead, nonstop advancements in AI algorithms and the integration of multi-omics data promise a period of accelerated timelines and substantiated drugs. As we stand at the nexus of invention and responsibility, the unborn geography of medicine discovery and development motions, driven by the pledge of AI to revise healthcare results with effectiveness, perfection, and case- centricity.

Keywords: Pharmacology, drug discovery, drug development, evolution, personalized medicines, computational models ethical considerations, multi-omics data, Innovation patient-centric health care

How to Cite

Revanth, B., Asrar , S. S., Sapkota , B., Vandana, K., Reddy, K. S., & Kumar , B. P. (2024). The Role of Artificial Intelligence and Machine Learning in Drug Discovery and Development. Asian Journal of Advances in Research, 7(1), 133–140. Retrieved from


Download data is not yet available.


1.Vihas Vijay, Sathish AS. Mapping Al's Impact on Pharmaceuticals: A Bibliometric Analysis, Vellore Institute of Technology, India. AI and IoT-Based Technologies for Precision Medicine; 2023.

Dahlin JL, Inglese J, Walters MA. Mitigating risk in academic preclinical drug discovery. Nature reviews Drug discovery. 2015;14(4):279-94.

Martell RE, Brooks DG, Wang Y, Wilcoxen K. Discovery of novel drugs for promising targets. Clinical therapeutics. 2013;35(9): 1271-81.

Kunduru AR. Machine Learning in Drug Discovery: A Comprehensive Analysis of Applications, Challenges, and Future Directions. International Journal on Orange Technologies. 2023;5(8):29-37.

Schneider G. Automating drug discovery. Nature reviews drug discovery. 2018;17 (2):97-113.

Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. 2021;57:101994.

Carpenter KA, Huang X. Machine learning-based virtual screening and its applications to Alzheimer's drug discovery: a review. Current pharmaceutical design. 2018;24 (28):3347-58.

Lysaght T, Lim HY, Xafis V, Ngiam KY. AI-assisted decision-making in healthcare: the application of an ethics framework for big data in health and research. Asian Bioethics Review. 2019;11:299-314.

Miller S, Moos W, Munk B, Munk S, Hart C, Spellmeyer D. Managing the Drug Discovery Process: Insights and advice for students, educators, and practitioners: Elsevier; 2 nd edtion,2023.

Ashiwaju BI, Orikpete OF, Uzougbo CG. The Intersection of Artificial Intelligence and Big Data in Drug Discovery: A Review of Current Trends and Future Implications. Matrix Science Pharma. 2023;7(2):36-42.

Ravina E. The evolution of drug discovery: from traditional medicines to modern drugs: John Wiley & Sons; 2011.

Davis AM, Plowright AT, Valeur E. Directing evolution: the next revolution in drug discovery? Nature Reviews Drug Discovery. 2017;16(10):681-98.

Moffat JG, Vincent F, Lee JA, Eder J, Prunotto M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nature reviews Drug discovery. 2017;16(8):531-43.

Li JJ, Corey EJ. Drug discovery: practices, processes, and perspectives: John Wiley & Sons; 2013.

Taylor D. The pharmaceutical industry and the future of drug development; 2015.

Rydzewski RM. Real world drug discovery: A chemist's guide to biotech and pharmaceutical research: Elsevier; 2010.

Roy SN, Mishra S, Yusof SM. Emergence of drug discovery in machine learning. Technical advancements of machine learning in healthcare. 2021:119-38.

Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Development Research; 2023.

Macher JT, Boerner C. Technological development at the boundaries of the firm: a knowledge‐based examination in drug development. Strategic management journal. 2012;33(9):1016-36.

Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nature reviews Drug discovery. 2016;15(7):473-84.

Rayhan R, Kinzler R. AI-Driven Advancements in Molecular Design: Revolutionizing Drug Discovery and Material Optimization.2023. DOI: 10.13140/RG.2.2.21580.80002

Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, et al. Exploiting machine learning for end-to-end drug discovery and development. Nature materials. 2019;18(5):435-41.

Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, et al. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Current drug targets. 2021;22(6):631-55.

Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nature reviews Drug discovery. 2019;18(6):463-77.

Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discovery Today. 2021;26(4):887-901.

Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Computational and structural biotechnology journal. 2016;14:177-84.

Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacological reviews. 2014; 66(1):334-95.

Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences. 2023;24(3):2026.

Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety. Trends in pharmacological sciences. 2019; 40(9):624-35.

Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023;15(7):1916.

Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends in pharmacological sciences. 2019;40 (8):577-91.

Blomme EA, Will Y. Toxicology strategies for drug discovery: present and future. Chemical research in toxicology. 2016;29 (4):473-504.

Kamila MK, Jasrotia SS. Ethical issues in the development of artificial intelligence: recognizing the risks. International Journal of Ethics and Systems; 2023.

Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, et al. AI applications to medical images: From machine learning to deep learning. Physica Medica. 2021;83:9-24.

Celi LA, Cellini J, Charpignon ML, Dee EC, Dernoncourt F, Eber R, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digital Health. 2022;1(3):e0000022.

Díaz-Rodríguez N, Del Ser J, Coeckelbergh M, de Prado ML, Herrera-Viedma E, Herrera F. Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion. 2023:101896.

Lamberti MJ, Wilkinson M, Donzanti BA, Wohlhieter GE, Parikh S, Wilkins RG, Getz K. A study on the application and use of artificial intelligence to support drug development. Clinical therapeutics. 2019; 41(8):1414-26.

Organization WH. Ethics and governance of artificial intelligence for health: WHO guidance; 2021. Available:

Husnain A, Rasool S, Saeed A, Hussain HK. Revolutionizing Pharmaceutical Research: Harnessing Machine Learning for a Paradigm Shift in Drug Discovery. International Journal of Multidisciplinary Sciences and Arts. 2023;2 (2):149-57.

Chopra H, Baig AA, Gautam RK, Kamal MA. Application of Artificial intelligence in Drug Discovery. Current Pharmaceutical Design. 2022;28(33):2690-703.

Turner Z. Edison to AI: Intellectual Property in AI-Driven Drug R&D 2023.

Lim D. AI & IP: innovation & creativity in an age of accelerated change. Akron L Rev. 2018;52:813.

Mak K-K, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug discovery today. 2019;24(3):773-80.

Winkler DA. Use of artificial intelligence and machine learning for discovery of drugs for neglected tropical diseases. Frontiers in Chemistry. 2021;9:614073.

Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Current pharmaceutical design. 2007;13(14):1497-508.

Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chemical reviews. 2019;119(18): 10520-94.

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug discovery today. 2021;26(1):80.

Jendoubi T. Approaches to integrating metabolomics and multi-omics data: a primer. Metabolites. 2021;11(3):184.

Tabata K. Digital Transformation: Accelerating Small-Molecule Drug Discovery. In the Lab eNewsletter. 2022; 17(12).

Mani G, Chen F, Cross S, Kalil T, Gopalakrishnan V, Rossi F, Stanley K. Artificial intelligence’s grand challenges: past, present, and future. AI Magazine. 2021;42(1):61-75.