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To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.<\/jats:p>","DOI":"10.1093\/bib\/bbad461","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T13:11:11Z","timestamp":1700745071000},"source":"Crossref","is-referenced-by-count":21,"title":["KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling"],"prefix":"10.1093","volume":"25","author":[{"given":"Qun","family":"Ren","sequence":"first","affiliation":[{"name":"Nanjing University of Chinese Medicine , 138 Xianlin Road, Nanjing 210023 , China"},{"name":"Drug Discovery and Design Center , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 , China"},{"name":"Shanghai Institute of Materia Medica, Chinese Academy of Sciences , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 , 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