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Omics-based large language models: A new engine for drug discovery innovation

文献类型：期刊论文

作者Sheng, Xia2,3; Zhang, Xiaoya2,3; Xing, Yuxin1,2,3; Shi, Yuqi2,3; Zeng, Chuanlong2,3; Tong, Xiaochu2,3; Zheng, Mingyue1,2,3; Li, Xutong2,3
刊名ACTA PHARMACEUTICA SINICA B
出版日期2026
卷号16期号:1页码:122-136
关键词Large language model Representation learning Generalization Omics integration Single-cell Perturbation modeling Target identification Drug discovery
ISSN号2211-3835
DOI10.1016/j.apsb.2025.10.034
英文摘要Traditional drug discovery suffers from low efficiency and high attrition rates, largely due to the complexity and heterogeneity of human diseases. Omics technologies offer a systems-level perspective for uncovering disease mechanisms and identifying therapeutic targets, but present challenges such as high dimensionality, noise, and heterogeneity. Large language models (LLMs), originally developed for natural language processing, are emerging as powerful tools to address these issues by capturing complex patterns and inferring missing information from large, noisy datasets. We present a three-part framework: (1) Analyzing how LLM architectures and learning paradigms handle challenges specific to genomics, transcriptomics, and proteomics data; (2) Detailing LLM applications in key areas: uncovering disease mechanisms, identifying drug targets, predicting drug response, and simulating cellular behavior; (3) Discussing how insights from omics-integrated LLMs can inform the development of drugs targeting specific pathways, moving beyond single targets towards strategies grounded in underlying disease biology. This framework provides both conceptual insights and practical guidance for leveraging LLMs in omics-driven drug discovery and development. (c) 2025 The Authors. Published by Elsevier B.V. on behalf of Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
WOS关键词VIRTUAL CELL ; DNA ; PREDICTION ; MOLECULES ; PROTEIN
资助项目Shanghai Municipal Science and Technology Major Project, National Key Research and Development Program of China[2023YFC2305904] ; Shanghai Municipal Science and Technology Major Project, National Key Research and Development Program of China[2022YFC3400504] ; Key Technologies R&D Program of Guangdong Province[2023B1111030004] ; Shanghai Sailing Program[24YF2755600] ; China Postdoctoral Science Foundation[2024M763421]
WOS研究方向Pharmacology & Pharmacy
语种英语
WOS记录号WOS:001664852400001
出版者INST MATERIA MEDICA, CHINESE ACAD MEDICAL SCIENCES
源URL[http://119.78.100.183/handle/2S10ELR8/322679]  
专题国家级研究中心_原创新药研究全国重点实验室
通讯作者Tong, Xiaochu; Zheng, Mingyue; Li, Xutong
作者单位1.Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 330106, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201103, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Sheng, Xia,Zhang, Xiaoya,Xing, Yuxin,et al. Omics-based large language models: A new engine for drug discovery innovation[J]. ACTA PHARMACEUTICA SINICA B,2026,16(1):122-136.
APA Sheng, Xia.,Zhang, Xiaoya.,Xing, Yuxin.,Shi, Yuqi.,Zeng, Chuanlong.,...&Li, Xutong.(2026).Omics-based large language models: A new engine for drug discovery innovation.ACTA PHARMACEUTICA SINICA B,16(1),122-136.
MLA Sheng, Xia,et al."Omics-based large language models: A new engine for drug discovery innovation".ACTA PHARMACEUTICA SINICA B 16.1(2026):122-136.

入库方式： OAI收割

来源：上海药物研究所

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