AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025470103
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REVIEW ARTICLE

Role of AI in drug development: Current status, challenges, opportunities, and future promise

Sheikh Usman Iqbal1* ,  Dmitriy Podolskiy2 ,  Mona G. Flores3 ,  Victoria L. Chiou4 ,  Jennifer Sheng5 ,  Roberto Araujo6 ,  Naveed Afzal7 ,  David A. Hall8 ,  Ingrid Vasiliu-Feltes9 ,  Dipu Patel10 ,  Manolis Kellis11,12
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1 Julz Pharma, Boston, United States of America
2 Astellas Pharma, Westborough, Massachusetts, United States of America
3 Independent researcher, Portland, Oregon, United States of America
4 Tempus AI, Inc., Chicago, Illinois, United States of America
5 College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America
6 Sanofi, Cambridge, Massachusetts, United States of America
7 Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, United States of America
8 AKT Health Inc., Cambridge, Massachusetts, United States of America
9 Department of Health Management and Policy, Herbert Business School, University of Miami, Coral Gables, Florida, United States of America
10 Department of PA Studies, School of Health and Rehabilitation Sciences, MGH Institute of Health Professions, Boston, Massachusetts, United States of America
11 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
12 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
AIH, 025470103 https://doi.org/10.36922/AIH025470103
Received: 18 November 2025 | Revised: 9 January 2026 | Accepted: 9 January 2026 | Published online: 13 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Artificial intelligence (AI) heralds a transformative shift in drug development, with speed, precision, and predictive power as its core features. Advances in systems-level biology platforms, coupled with substantial investments in generative AI-centric pharma integration, have fostered healthy optimism among stakeholders about identifying new cures through renewed approaches and improved productivity. However, navigating epistemological, ethical, patient safety, and ontological dimensions within research and development (R&D) presents challenges that AI must address to enhance its mainstream adoption and practical utility. Here, multidisciplinary experts discuss key applications of AI across the full continuum of drug development, examine the challenges encountered, and propose solution frameworks. Drug development remains fraught with unknown biology, patient heterogeneity, and perplexing therapeutic risks. Stringent regulatory and compliance guidelines further necessitate that conventional pharma processes, practices, and strategies remain paramount in R&D execution, while guiding the integration of AI in a “value-for-effort,” evidence-based, yet Promethean fashion.

Graphical abstract
Keywords
Generative artificial intelligence
Machine learning
Drug discovery
Synthetic control arms
Digital twins
AI-driven clinical research
Funding
None.
Conflict of interest
Sheikh Usman Iqbal is a CMO consultant for Julz Pharma. Dmitriy Podolskiy is an employee of Astellas Pharma. Mona G. Flores is self-employed. Victoria L. Chiou is an employee of Tempus AI, Inc. Roberto Araujo is an employee of Sanofi. Naveed Afzal is an employee of Takeda Pharmaceutical Company Limited. David A. Hall is an employee of AKT Health Inc. The authors declare no other competing interests beyond the relationships stated above.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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