Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

Weikaixin Kong; Wenyu Wang; Jinbing An

doi:10.1016/j.compbiolchem.2020.107303

Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

Comput Biol Chem. 2020 Jun 6:87:107303. doi: 10.1016/j.compbiolchem.2020.107303. Online ahead of print.

Authors

Weikaixin Kong¹, Wenyu Wang², Jinbing An³

Affiliations

¹ Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China.
² School of Nursing, Peking University, Beijing, 100191, China.
³ Department of Health Informatics and Management, Peking University, Beijing, 100191, China. Electronic address: termison@sina.cn.

PMID: 32563857
DOI: 10.1016/j.compbiolchem.2020.107303

Abstract

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Machine learning methods can be used in ligand-based activity prediction processes. In order to predict SERT inhibitors, the SERT inhibitor data from the ChEMBL database was screened and pre-processed. Then 4 machine learning methods (LR, SVM, RF, and KNN) and 4 molecular fingerprints (CDK, Graph, MACCS, and PubChem) were used to build 16 prediction models. The top 5 models of accuracy (Q) in the cross-validation of training set were used to build three different ensemble learning models. In the test1 set, the VOT_CLF3 model had the largest SP (0.871), Q (0.869), AUC (0.919), and MCC (0.728). In the unbalanced test2 set, VOT_CLF3 had the largest SE (0.857), SP (0.867), Q (0.865) and MCC (0.639). VOT_CLF3 was recommended for the virtual screening process of SERT inhibitors. In addition, 12 molecular structural alerts that frequently appear in SERT inhibitors were found (P < 0.05), which provided important reference value for the design work of SERT inhibitors.

Keywords: 5-Hydroxytryptamine transporter; Depression; Ensemble model; Inhibitor; Machine learning.