Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach  

作　　者：Lingjie Bao Zhe Wang Zhenxing Wu Hao Luo Jiahui Yu Yu Kang Dongsheng Cao Tingjun Hou 

机构地区：[1]Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University,College of Pharmaceutical Sciences,Zhejiang University,Hangzhou 310058,China [2]State Key Lab of CAD&CG,Zhejiang University,Hangzhou 310058,China [3]Xiangya School of Pharmaceutical Sciences,Central South University,Changsha 410013,China

出　　处：《Acta Pharmaceutica Sinica B》2023年第1期54-67,共14页药学学报（英文版）

基　　金：financially supported by National Key Research and Development Program of China(2021YFF1201400);National Natural Science Foundation of China(21575128,81773632,22173118);Natural Science Foundation of Zhejiang Province(LZ19H300001,China);Science and Technology Innovation Program of Hunan Province(2021RC4011,China)。

摘　　要：Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development,such as lead discovery,drug repurposing and elucidation of potential drug side effects.Therefore,a variety of machine learning-based models have been developed to predict these interactions.In this study,a model called auxiliary multi-task graph isomorphism network with uncertainty weighting(AMGU)was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network(MT-GIN)with the auxiliary learning and uncertainty weighting strategy.The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks(GNN)models on the internal test set.Furthermore,it also exhibited much better performance on two external test sets,suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity.Then,a naÏve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms,and the consistency of the interpretability results for 5typical epidermal growth factor receptor(EGFR)inhibitors with their structure-activity relationships could be observed.Finally,a free online web server called KIP was developed to predict the kinomewide polypharmacology effects of small molecules(http://cadd.zju.edu.cn/kip).

关 键 词：Kinome-wide polypharmacology Machine learning KINASES Graph neural networks Artificial intelligence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R96[自动化与计算机技术—控制科学与工程]