The reviewed record of science sign in
Pith

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

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

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:UKVSQH3Srecord.jsonopen to challenge →

classification q-bio.QM cs.AIcs.LG
keywords drugdevelopmentdiscoveryclinicalllmsmodelscomputationaldisease
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design

    cs.AI 2026-05 unverdicted novelty 5.0

    LipoAgent coordinates fine-tuned LLM agents with a toxicity-first conditional prediction to improve lipid design for safer mRNA delivery, reporting 32% relative gains and wet-lab confirmation of virtual rankings.

  2. The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences

    cs.CL 2025-09 unverdicted novelty 3.0

    The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.