FAQ: What You Need to Know About Agentic AI in Drug Discovery

1. What is agentic AI in the biopharmaceutical industry?

Agentic AI refers to autonomous artificial intelligence systems that can reason, plan, and execute multi-step workflows with minimal direct human intervention. In the life sciences sector, agentic systems unite reasoning, use of tools, and task execution to accelerate complex R&D processes, such as target prioritization and compound optimization. The Pistoia Alliance, for example, characterizes agentic AI as one of the most disruptive emerging technologies with potential to change how scientists work and innovate in biopharma (1).

2. What’s the importance of agentic AI and “closed-loop” drug discovery to the biopharma industry?

Agentic AI, especially in closed-loop drug discovery, can create feedback-driven workflows in which hypotheses are generated, tested in-silico, evaluated, and refined without waiting for human prompts at every step. This “closed loop” speeds up early discovery, enhances reproducibility, and can compress multi-step research cycles into shorter timeframes. Because agentic AI systems connect reasoning, planning, and action, companies view them as essential for productivity gains across R&D and early drug discovery (1).

3. What are the benefits of agentic AI in healthcare?

Agentic AI in healthcare promises several advantages, including:

• Accelerated research cycles, which reduces the time needed for tasks, such as target identification, molecule design, and optimization.
• Workflow automation that enables data analysis, experiment planning, and predictive modeling without repeated manual intervention.
• Improved decision support, which can enhance clinical and regulatory decisions by integrating real-time data and simulation outcomes.
• Resource efficiency, which allows scientists to focus on strategic scientific thinking while AI handles routine or combinatorial work.

4. What is the future scope of AI in drug discovery?

AI’s role in drug discovery is poised to expand rapidly. Industry reports suggest that AI isn’t just a niche tool but is projected to transform discovery and development pipelines by 2030, with over half of new drugs potentially discovered or developed using AI methods (2). Beyond molecule identification, AI is expected to improve clinical trial design, patient recruitment, regulatory analytics, and manufacturing optimization.

5. What drugs have been discovered by AI to date?

AI-driven discovery has already produced several noteworthy candidates, including rentosertib (formerly ISM001-055, Insilico Medicine), a novel small molecule designed entirely by generative AI that reached clinical investigation for idiopathic pulmonary fibrosis, with both its biological target and compound identified through AI (3); DSP-0038 (under joint research by Sumitomo Pharma and Recursion [formerly Exscientia[), an AI-developed serotonin receptor modulator for neuropsychiatric conditions entered early clinical testing (4); and Halicin (Massachusetts Institute of Technology), a novel antibiotic identified via deep learning algorithms (5). These examples illustrate how AI is transitioning from computational pipelines to real investigational drug candidates.

6. What is the success rate of AI drug discovery?

Emerging evidence indicates that AI-designed drug candidates are showing higher early-stage clinical success rates compared with traditional approaches. Analyses report that AI-discovered molecules have achieved Phase I clinical trial success rates in the range of approximately 80–90%, significantly above traditional historical averages of 40–65%. While data for later stages (Phase II/III) are still limited, these early indicators suggest AI is improving the probability that promising candidates progress through development (6).

References

1. Pistoia Alliance. Pistoia Alliance Unveils Agentic AI Initiative and Seeks Industry Funding to Drive Safe Adoption. Press Release. Sept. 4, 2025.XXXX
2. CPHI. The 2023 CPHI Annual Report. October 2023.
3. Xu, Z.; Ren, F.; Wang, P.; et al. A Generative AI-discovered TNIK Inhibitor for Idiopathic Pulmonary Fibrosis: A Randomized Phase 2a Trial. Nat. Med. 2025, 31, 2602–2610. DOI: 10.1038/s41591-025-03743-2
4. Sumitomo Pharma. Profiles of Major Products under Development. sumitomo-pharma.com (accessed Jan. 30, 2026).
5. Trafton, A. Artificial Intelligence Yields New Antibiotic. news.mit.edu. Feb. 20, 2020.
6. Tilawat, M. AI in Drug Development Statistics 2026: The $60 Billion Reality vs. Hype Analysis. allaboutai.com. Updated Jan. 1, 2026 (accessed Jan. 30, 2026).