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Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials

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arxiv 2409.04481 v1 pith:UKVSQH3S submitted 2024-09-06 q-bio.QM cs.AIcs.LG

Yizhen Zheng , Huan Yee Koh , Maddie Yang , Li Li , Lauren T. May , Geoffrey I. Webb , Shirui Pan , George Church This is my paper · ORCID
classification q-bio.QMcs.AIcs.LG
keywords drugdevelopmentdiscoveryclinicalllmsmodelscomputationaldisease
verification ladder T0 review T1 audit T2 compute T3 formal
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The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 14 citations worldwide. Full citation record

  1. CrystalICL: Enabling In-Context Learning for Crystal Generation

    cs.LG 2025-08 conditional novelty 6.0 of 10

    CrystalICL is a few-shot crystal generation model combining space-group tokenization with multi-task instruction tuning.

  2. DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search

    cs.AI 2025-07 conditional novelty 6.0 of 10

    A multi-agent, retrieval-augmented search framework lets a 7B language model outperform larger baselines on drug-target interaction prediction, but its headline recall relies on knowing the ground-truth output count.

  3. Tokenized Bandit for LLM Decoding and Alignment

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Under a new structural assumption (DDMC), token-level bandit algorithms achieve sublinear regret and greedy LLM decoding is shown to be near-optimal.

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