Machine learning methods for developments of binding kinetic models in predicting protein-ligand dissociation rate constants  

作　　者：Yujing Zhao Qilei Liu Jian Du Qingwei Meng Lei Zhang 

机构地区：[1]State Key Laboratory of Fine Chemical,Frontiers Science Center for Smart Materials Oriented Chemical Engineering,Institute of Chemical Process Systems Engineering,School of Chemical Engineering,Dalian University of Technology,Dalian,China [2]Ningbo Institute of Dalian University of Technology,Ningbo,China

出　　处：《Smart Molecules》2023年第3期98-112,共15页智能分子（英文）

基　　金：financial supports of“the Fundamental Research Funds for the Central Universities”(DUT22YG218),NSFC(22278053,22078041);China Postdoctoral Science Foundation(2022M710578);“the Dalian High-level Talents Innovation Support Program”(2021RQ105).

摘　　要：Binding kinetic properties of protein–ligand complexes are crucial factors affecting the drug potency.Nevertheless,the current in silico techniques are insufficient in providing accurate and robust predictions for binding kinetic properties.To this end,this work develops a variety of binding kinetic models for predicting a critical binding kinetic property,dissociation rate constant,using eight machine learning(ML)methods(Bayesian Neural Network(BNN),partial least squares regression,Bayesian ridge,Gaussian process regression,principal component regression,random forest,support vector machine,extreme gradient boosting)and the descriptors of the van der Waals/electrostatic interaction energies.These eight models are applied to two case studies involving the HSP90 and RIP1 kinase inhibitors.Both regression results of two case studies indicate that the BNN model has the state-of-the-art prediction accuracy(HSP90:R^(2)_(test)=0:947,MAE_(test)=0.184,rtest=0.976,RMSE_(test)=0.220;RIP1 kinase:R^(2)_(test)=0:745,MAE_(test)=0.188,rtest=0.961,RMSE_(test)=0.290)in comparison with other seven ML models.

关 键 词：Bayesian neural network binding kinetics dissociation rate constant machine learning protein-ligand interaction energies 

分 类 号：O64[理学—物理化学]