mlsanityData to 7 Oct 2026

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

arXiv 2409.04481 ↗First posted 6 Sep 2024q-bio.QM cs.AI cs.LGcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 86 of its 219 references that are in the map. First posted on arXiv 6 Sep 2024.

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86 of 219 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 21; 2026: 1.

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  1. 2025LLMs for Explainable AI: A Comprehensive Surveycited by 42
  2. 2025DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discoverycited by 18
  3. 2025BiMark: Unbiased Multilayer Watermarking for Large Language Modelscited by 18
  4. 2025Dynamic and Generalizable Process Reward Modelingcited by 16
  5. 2024Causal Representation Learning from Multimodal Biomedical Observationscited by 12
  6. 2025A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimizationcited by 12
  7. 2025Collaborative Expert LLMs Guided Multi-Objective Molecular Optimizationcited by 11
  8. 2025mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Moleculescited by 8
  9. 2025Zephyrus: An Agentic Framework for Weather Sciencecited by 8
  10. 2025CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Predictioncited by 5
  11. 2025Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMscited by 5
  12. 2025DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Searchcited by 3
12 more
  1. 2025Can Hallucinations Help? Boosting LLMs for Drug Discoverycited by 2
  2. 2025KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Modelcited by 2
  3. 2025Tokenized Bandit for LLM Decoding and Alignmentcited by 2
  4. 2025CrystalICL: Enabling In-Context Learning for Crystal Generationcited by 2
  5. 2025Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoningcited by 2
  6. 2025Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspectivecited by 1
  7. 2025M2LLM: Multi-view Molecular Representation Learning with Large Language Modelscited by 1
  8. 2024Exploiting Latent Linearity in LLMs Improves Explainable Molecular Representation Learningcited by 0
  9. 2025SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generationcited by 0
  10. 2025From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biologycited by 0
  11. 2025The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciencescited by 0
  12. 2026LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Designcited by 0