biostudies-literatureKoyama TGrant-in-Aid for Transformative Research Areas (A) “Latent Chemical Space”Grant-in-Aid for Transformative Research Areas (A) "Latent Chemical Space"Japan Agency for Medical Research and DevelopmentJapan Society for the Promotion of Science29https://www.ebi.ac.uk/biostudies/studies/S-EPMC12922394biostudies-literatureUnknown18(1)Accurate prediction of compound-protein interactions (CPIs) is crucial for chemical biology and drug discovery. Despite recent advancements, existing deep learning (DL)-based CPI models often struggle to simultaneously achieve high generalization performance, quantify prediction confidence, and ensure explainability. Here, we propose ChemGLaM, a chemical genomics language model designed to address these three crucial challenges, thereby enabling reliable and explainable CPI predictions. ChemGLaM integrates independently pre-trained chemical and protein language models through an interaction block with a cross-attention mechanism, achieving near state-of-the-art performance in predicting novel CPIs at a low computational cost. Incorporating uncertainty estimation and attention visualizationJournal of cheminformaticsChemical genomics language model toward reliable and explainable compound-protein interaction exploration.PMC12922394JP24H01771JP22bm080403422K06112JP24KJ1510Matsumoto SKojima RKoyama THasegawa AOkuno YYamazaki KImamura KOkita RIwata HInoue HKato TTsumura HfalseChemical genomics language model toward reliable and explainable compound-protein interaction exploration.Accurate prediction of compound-protein interactions (CPIs) is crucial for chemical biology and drug discovery. Despite recent advancements, existing deep learning (DL)-based CPI models often struggle to simultaneously achieve high generalization performance, quantify prediction confidence, and ensure explainability. Here, we propose ChemGLaM, a chemical genomics language model designed to address these three crucial challenges, thereby enabling reliable and explainable CPI predictions. ChemGLaM integrates independently pre-trained chemical and protein language models through an interaction block with a cross-attention mechanism, achieving near state-of-the-art performance in predicting novel CPIs at a low computational cost. Incorporating uncertainty estimation and attention visualization2026-01-01T00:00:00Z2026 Jan2026-07-16T15:11:25.323Z2026-07-09T11:03:23.276ZS-EPMC129223944161838510.1186/s13321-026-01155-z