文献类型：期刊文献

英文题名：MPSM-DTI: prediction of drug-target interaction via machine learning based on the chemical structure and protein sequence

作者：Peng, Yayuan[1];Wang, Jiye[1];Wu, Zengrui[1];Zheng, Lulu[1];Wang, Biting[1];Liu, Guixia[1];Li, Weihua[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2022

卷号：1

期号：2

起止页码：115

外文期刊名：DIGITAL DISCOVERY

收录：WOS:【ESCI(收录号:WOS:001118869500001)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2019YFA0904800), the National Natural Science Foundation of China (Grants 81872800 and 82173746), the China Postdoctoral Science Foundation (Grant 2019M661413), and Shanghai Sailing Program (Grant 19YF1412700).

语种：英文

摘要：Drug-target interaction (DTI) plays a central role in drug discovery. How to predict DTI quickly and accurately is a key issue. Traditional structure-based and ligand-based methods have some inherent deficiencies. Hence, it is necessary to develop a new method for DTI prediction that does not rely on crystal structures of protein targets or quantity and diversity of ligands. In this study, we collected 40 898 DTIs with k(d) values from ChEMBL 27 to develop a prediction method. Through data standardization, SMOTE sampling and pipeline techniques, among 30 models the Morgan-PSSM-SVM model (MPSM-DTI) was demonstrated as the best one with ten-fold cross-validation (F-1 = 85.55 +/- 0.46%, R = 84.89 +/- 0.62% and P = 86.24 +/- 0.81%) and test set validation (F-1 = 85.11%, R = 84.34% and P = 85.90%). The results in two external validation sets indicated that the MPSM-DTI model had satisfactory generalization capability and could be used in target prediction for new compounds. Specifically, the F-1, P and R values were 83.27%, 85.21% and 81.41% in external validation set 1 and 86.45%, 87.50% and 85.42% in external validation set 2. Via the latest literature evidence, we collected 100 new DTIs of eight GPCR targets to prove that MPSM-DTI could predict compounds for protein targets without known ligands and crystal structures. Compared with other DTI prediction methods, our method reached considerable accuracy and addressed the dilemma of DTI prediction for brand new protein targets. Furthermore, we proposed the pipeline encapsulation technique, which would avoid data leak and improve generalization ability of the model. The source code of the method is available at https://github.com/pengyayuan/MPSM-DTI.