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

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.

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