Application of artificial intelligence large language models in drug target discovery

作者：Xinyu Liu, Jiafan Zhang, Xiaoran Wang, Maoda Teng, Guoying Wang, Xiaoming Zhou · 发表于：Frontiers in Pharmacology · 年份：2025 · DOI：10.3389/fphar.2025.1597351 · 被引用次数：13 · 研究领域：Topic Modeling、Machine Learning in Bioinformatics、Biomedical Text Mining and Ontologies

Drug target discovery is a fundamental aspect of contemporary drug research and development. However, the use of conventional biochemical screening, omics analysis, and related approaches is constrained by substantial technical complexity and significant resource requirements. With the advancement of artificial intelligence-based large language models, notable progress has been achieved in drug target identification. During target mining, large language models with natural language comprehension capabilities can efficiently integrate literature data resources and systematically analyze disease-associated biological pathways and potential targets. Notably, models specifically designed for biomolecular "language" have demonstrated advantages across multiple aspects. The genomics-focused large language model has significantly enhanced the accuracy of pathogenic gene variant identification and gene expression prediction. In transcriptomics, large language models enable comprehensive reconstruction of gene regulatory networks. In proteomics, advancements have been made in protein structure analysis, function prediction, and interaction inference. Additionally, the single-cell multi-omics large language model facilitates data integration across different omics technologies. These technological advancements provide multi-dimensional biological evidence supporting drug target discovery and contribute to a more efficient screening process for candidate targets. The development of thes...