The evolving landscape of agentic drug discovery is critically examined, illustrating the transition toward hybrid human AI intelligence, digital twins, and regulation-ready autonomous platforms.
Abstract
Drug discovery is undergoing a paradigm shift from isolated artificial intelligence (AI) tools to integrated, closed-loop, agent-driven systems that combine prediction with experimental execution. Recent advances in large language models (LLMs), generative frameworks, and self-driving systems are facilitating the emergence of adaptive, multi-agent ecosystems capable of hypothesis generation, iterative optimisation, and autonomous decision-making. Despite this progress, key challenges, including data bias, limited interpretability, coordination fragility, and regulatory misalignment, constrain the translation into reliable clinical outcomes. Here, the evolving landscape of agentic drug discovery is critically examined, illustrating the transition toward hybrid human AI intelligence, digital twins, and regulation-ready autonomous platforms. Resolving these challenges will determine whether the field remains tool-driven or advances into a truly self-evolving scientific enterprise.
Accelerating the identification of viable drug candidates across therapeutic areas demands autonomous systems for molecular generation that enforce chemical correctness by structural invariants rather than model capability. We present MolecureAI, an autonomous AI agent architecture for closed-loop drug discovery with c...
M. Chadi, Asmaa Amzil, Hajar Ezzahoud et al.· Frontiers in Pharmacology· 0 citations
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
The Virtual Biotech is introduced, an organization of artificial intelligence agents modeled on a drug-development company, with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development, which demonstrates its utility at three drug-development decision points.
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Qian Yong, Chun-Yang Chen, Xi-Meng Zhang et al.· International journal of pha...· 2 citations
Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug deve...
Zhi-Meng Zhou, Yang Nan, Min-Jie Mou et al.· bioRxiv· 0 citations
A structured view of current advances, persistent gaps, and future directions in AI-enabled pharmaceutical innovation is offered, showing growing convergence between AI and precision therapeutics, but real-world application demands robust validation, collaboration, explainable AI, and global standards.
M. Fareed, S. Shityakov· Quantum Machine Intelligence· 0 citations
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