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Cited by 202Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

AI-Driven Drug Discovery: A Comprehensive Review

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Ferreira, Fábio J. N., Carneiro, Agnaldo S.·2025·ACS Omega
DOI 10.1021/acsomega.5c00549PMC PMC12177741
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Abstract

Artificial intelligence (AI) and machine learning (ML) offer transformative potential to address the persistent challenges of traditional drug discovery, characterized by high costs, lengthy timelines, and low success rates. This comprehensive review critically analyzes recent advancements (2019-2024) in AI/ML methodologies across the entire drug discovery pipeline, from target identification to clinical development. We examine diverse AI techniques, including deep learning, graph neural networks, and transformers, focusing on their application in key areas such as target identification, lead discovery, hit optimization, and preclinical safety assessment. Our in-depth comparative analysis highlights the advantages, limitations, and practical challenges associated with different AI approaches, emphasizing critical factors for successful implementation such as data quality, model validation, and ethical considerations. The review synthesizes current applications, identifies persistent gapsparticularly in data accessibility, interpretability, and clinical translationand proposes future directions to unlock the full potential of AI in creating safer, more effective, and accessible medicines. By emphasizing transparent methodologies, robust validation, and ethical frameworks, this review aims to guide the responsible and impactful integration of AI into pharmaceutical research and development.

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  • The value of agentic AI in drug discovery and personalized medicine emerges when reasoning, action, and evidence monitoring operate as an integrated system capable of accelerating insight without compromising reproducibility or governance.

    “As recent implementations indicate, the value of agentic AI in drug discovery and personalized medicine emerges when reasoning, action, and evidence monitoring operate as an integrated system capable of accelerating insight without compromising reproducibility or governance [27, 33, 34, 45–48].”

    From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [46]

    The Review of Findings and Discussions (Compare and Contrast; page unavailable) says AI can accelerate drug development but requires transparency and bias-control frameworks. The Conclusion (Summary of Key Points; page unavailable) also calls for validation and ethical implementation. Neither passage describes agentic AI or an integrated system of reasoning, action, and evidence monitoring, so the source supports the broader acceleration-and-safeguards point but not the claim’s specific mechanism. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Generative modeling frameworks are increasingly used to propose individualized therapeutic hypotheses or optimized drug combinations in the presence of explicit clinical constraints.

    “Generative modeling frameworks, which often integrate sequence and graph transformers with latent variable representations, are increasingly used to propose individualized therapeutic hypotheses or optimized drug combinations in the presence of explicit clinical constraints [45, 46].”

    From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [46]

    The Review of Findings (Theme 2; page unavailable) describes diffusion-based generative modeling for molecular docking, and the Conclusion (Future Research Directions; page unavailable) calls for clinically relevant endpoints and targeted therapies. It does not say that generative frameworks are increasingly used to propose individualized therapeutic hypotheses or optimized drug combinations under explicit clinical constraints. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • By explicitly tracing the provenance of evidence and ranking candidate hypotheses, such systems can reduce the time required for structured review from weeks to hours, while preserving verifiability and reproducibility.

    “By explicitly tracing the provenance of evidence and ranking candidate hypotheses, such systems can reduce the time required for structured review from weeks to hours, while preserving verifiability and reproducibility.”

    From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [46]

    The Methods sections on Study Selection Process and Quality Assessment/Risk of Bias describe this review’s screening procedures and emphasis on reproducibility; no page numbers are available in our copy. Searches for evidence provenance, hypothesis ranking, and a reduction in structured-review time from weeks to hours found no passage addressing those claims. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Multi-agent systems address the information bottleneck by combining retrieval-enhanced generation with role specialization, in which sub-agents focus on distinct domains under the supervision of an orchestrator.

    “Multi-agent systems address this bottleneck by combining retrieval-enhanced generation with role specialization, in which sub-agents focus on distinct domains (e.g. mapping mechanisms of action, profiling adverse events, structural chemistry) under the supervision of an orchestrator [33, 45, 46].”

    From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [46]

    The Theoretical Framework discusses GNNs and Transformers, while the review’s lead-discovery section discusses LLMs with multisource prompting; neither describes the claimed multi-agent arrangement. Searches for multi-agent systems, retrieval-augmented generation, specialized agents, and an orchestrator found no passage addressing that arrangement in our copy. Page numbers were not available. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Ferreira, F. J. N., & Carneiro, A. S.
Affiliations
Universidade Federal do Pará (BR)
Corresponding author
Carneiro, Agnaldo S.
ORCID
Ferreira, Fábio J. N.
Venue
ACS Omega
Publisher
American Chemical Society (ACS)
Published
2025-06-06
Volume
10
Issue
23
Pages
23889-23903
ISSN
2470-1343 (ISSN-L)
DOI
10.1021/acsomega.5c00549
PMC
PMC12177741
OpenAlex
W4411095946
Cited by
202
References
3
Citations by year
2026: 170 · 2025: 32
FWCI
12.46
Type
journal article
Language
en
Open access
Licence
Locations
  • ACS Omega (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence in drug discovery, machine learning in drug discovery, target identification, lead compound identification, hit optimization, preclinical safety, deep learning, graph neural networks, transformer models, model validation, data quality, interpretability, clinical translation, ethical considerations, pharmaceutical R&D

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