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Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives.
Attila A Seyhan1, Claudio Carini2
1Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert, Medical School, Brown University, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, United States; Joint Program in Cancer Biology, Brown Health System and Brown University, Providence, RI, United States; Legorreta Cancer Center at Brown University, Providence, RI, United States.
Critical Reviews in Oncology/Hematology
|June 27, 2026
Summary
Artificial intelligence (AI) is revolutionizing drug discovery by accelerating exploration of chemical spaces and optimizing therapeutic candidates. Challenges remain in data quality and experimental validation for AI-designed drugs to reach market approval.
Area of Science:
- Computational chemistry and pharmacology
- Biotechnology and pharmaceutical sciences
- Machine learning in healthcare
Background:
- Traditional drug discovery is hampered by long timelines, high costs, and significant attrition rates.
- Artificial intelligence (AI) offers transformative potential to overcome these limitations.
- Generative modeling and advanced AI platforms are expanding the scope of drug research.
Purpose of the Study:
- To review the evolution of AI approaches in drug discovery and development.
- To highlight key challenges and opportunities in AI-driven therapeutic innovation.
- To examine AI's impact across the R&D pipeline, from target identification to clinical decision-making.
Main Methods:
- Analysis of recent advances in AI, including generative modeling, scaffold-aware design, 3D molecular design tools (e.g., AlphaFold, MoleR, PocketCrafter), single-cell foundation models, and large language models (LLMs).
- Integration of AI into preclinical and clinical workflows, such as adaptive trial design and AI-driven drug repurposing.
- Examination of regulatory landscape (e.g., U.S. FDA approvals) and clinical progression of AI-originated candidates.
Main Results:
- AI accelerates exploration of chemical and biological spaces, improving molecular modeling and candidate optimization.
- Emerging AI platforms enhance target identification, lead optimization, phenotypic screening, and precision biology.
- Several AI-originated drug candidates are in clinical development, demonstrating translational impact.
- AI acts as a collaborative tool, uncovering novel designs and informing downstream development.
Conclusions:
- AI is a powerful collaborative partner in drug discovery, enhancing efficiency, scalability, and cost-effectiveness.
- Broader AI impact hinges on high-quality multimodal data, robust regulatory/ethical frameworks, and acknowledging methodological limits.
- AI is poised to significantly shape the future of data-driven therapeutic innovation.
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