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AI-Driven Retrosynthesis Framework for Drug Discovery: The Use of LLMs

Journal of Biomedical Research & Environmental Sciences article abstract with citation details, DOI, publication dates, subject areas, full text links, and references.

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Open Access
Article Type Mini Review
Subject Biology Group
OCLC JBRES Record
David Joshua Ferguson*
Issue: Volume6-Issue5
Pages: 556-562
Received: 2025-05-16
Accepted: 2025-05-24
Published: 2025-05-28

Abstract

The process of retrosynthetic analysis, introduced by Corey, systematically deconstructs complex molecules into simpler precursors, providing a logical pathway for chemical synthesis. Here, we propose an innovative AI-driven retrosynthesis framework for drug discovery leveraging Large Language Models (LLMs) and advanced computational tools. This "retro drug discovery" platform integrates AlphaFold2-generated protein structures, MolGPT-driven scaffold generation, and a tailored ChatGPT model orchestrating Structure-Activity Relationship (SAR) analyses, virtual screening, and iterative optimization cycles. We applied this framework retrospectively to twenty FDA-approved small-molecule drugs spanning cardiovascular, neurological, oncology, and endocrine therapeutic areas. Each case study illustrates how AI systems can recapitulate historical discovery pathways with high fidelity, as demonstrated by metrics including structural similarity (average Tanimoto coefficient ≈ 0.82) and bioactivity-prediction concordance (mean Pearson r ≈ 0.78). The methodology emphasizes bioisosteric replacements, scaffold hopping, and pharmacophore optimization, reflecting human medicinal-chemistry strategies. The implementation of an AI-driven retrosynthetic platform, "ChemGPT Discover," exemplifies automation of medicinal-chemistry processes, enhancing efficiency in hit-to-lead development. Our results validate the capability of LLM-assisted retrosynthesis to rediscover known drug leads accurately, underscoring the transformative potential of AI in accelerating drug discovery and medicinal chemistry research.

FullText HTML FullText PDF DOI: 10.37871/jbres2110 Crossmark

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Copyright

© 2025 Ferguson DJ, Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Ferguson DJ. AI-Driven Retrosynthesis Framework for Drug Discovery: The Use of LLMs. J Biomed Res Environ Sci. 2025 May 28; 6(5): 556-562. doi: 10.37871/jbres2110, Article ID: JBRES2110, Available at: https://www.jelsciences.com/ articles/jbres2110.pdf

Subject area(s)

BiomedicineBioinformaticsBiotechnology

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