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

2024/09/06 by Yizhen Zheng, Zheng, Yizhen, Huan Yee Koh +12 · 9 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Genetics, Bioinformatics, and Biomedical Research #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.2409.04481

openalex publication_date 2024/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning
• Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
• Zephyrus: An Agentic Framework for Weather Science
• The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences
• CrystalICL: Enabling In-Context Learning for Crystal Generation
• From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology
• M²LLM: Multi-view Molecular Representation Learning with Large Language Models
• Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective
• CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction

Related

• Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development
• Tx-LLM: A Large Language Model for Therapeutics
• Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
• Large language models and their applications in bioinformatics
• Large Language Model Agent for Modular Task Execution in Drug Discovery