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Peer-reviewedCC BY-NCBriefings in bioinformatics

Can autonomous Agentic-AI systems accelerate drug discovery and development in the next decade?

  • Navaneet Chaturvedi1
  • Shafiul Haque2,3
  • Vijay Kumar4
4 affiliations
  1. 1Amity Institute of Biotechnology, Amity University, Sector-125, Amity Road, Noida, Uttar Pradesh 201303, India.
  2. 2Health Research Center, and Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 82911, Saudi Arabia.
  3. 3School of Medicine, Universidad Espiritu Santo, Samborondon 091952, Ecuador.
  4. 4Department of Biotechnology, Anand School of Engineering and Technology, Sharda University Agra, Uttar Pradesh 282007, India.
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Abstract

The pharmaceutical industry is experiencing a profound transformation driven by rapid advancements in artificial intelligence (AI), increasing research and development complexity, and the expiration of key patents. Agentic-AI systems, which enable autonomous reasoning, decision-making, and adaptive learning beyond traditional data-driven automation, represent a paradigm shift in this context. This review examines the impact of Agentic-AI across the pharmaceutical value chain, including supply chain orchestration, manufacturing optimization, early-stage drug discovery, process development, and regulatory intelligence. These systems enhance operational resilience, reduce development timelines, and accelerate the identification of new therapeutic candidates by fostering dynamic human-AI collaboration. However, the widespread adoption of Agentic-AI introduces significant challenges related to workforce integration, ethical accountability, governance, and regulatory compliance. Addressing these challenges is essential for reliable and scalable deployment. This study identifies key factors shaping Agentic-AI's influence over the next decade and provides a forward-looking perspective on its evolving role in pharmaceutical innovation.

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Cite

BibTeXRIS
BibTeX
@article{chaturvedi2026autonomous,
  title = {{Can autonomous Agentic-AI systems accelerate drug discovery and development in the next decade?}},
  author = {Navaneet Chaturvedi and Shafiul Haque and Vijay Kumar},
  journal = {Briefings in bioinformatics},
  year = {2026},
  month = sep,
  doi = {10.1093/bib/bbag583},
  url = {https://europepmc.org/article/MED/42905768},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Can autonomous Agentic-AI systems accelerate drug discovery and development in the next decade?
AU  - Navaneet Chaturvedi
AU  - Shafiul Haque
AU  - Vijay Kumar
PY  - 2026
DA  - 2026/09/01
JO  - Briefings in bioinformatics
DO  - 10.1093/bib/bbag583
UR  - https://europepmc.org/article/MED/42905768
AB  - The pharmaceutical industry is experiencing a profound transformation driven by rapid advancements in artificial intelligence (AI), increasing research and development complexity, and the expiration of key patents. Agentic-AI systems, which enable autonomous reasoning, decision-making, and adaptive learning beyond traditional data-driven automation, represent a paradigm shift in this context. This review examines the impact of Agentic-AI across the pharmaceutical value chain, including supply chain orchestration, manufacturing optimization, early-stage drug discovery, process development, and regulatory intelligence. These systems enhance operational resilience, reduce development timelines, and accelerate the identification of new therapeutic candidates by fostering dynamic human-AI collaboration. However, the widespread adoption of Agentic-AI introduces significant challenges related to workforce integration, ethical accountability, governance, and regulatory compliance. Addressing these challenges is essential for reliable and scalable deployment. This study identifies key factors shaping Agentic-AI's influence over the next decade and provides a forward-looking perspective on its evolving role in pharmaceutical innovation.
SN  - 1467-5463
SN  - 1477-4054
ER  -