Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning. (August 2020)

• Record Type: Journal Article
• Authors: Kong, Weikaixin; Wang, Wenyu; An, Jinbing
• Is Part Of: Computational biology and chemistry. Volume 87(2020)
• Publication Date: 2020-08
• Subjects: Depression -- 5-Hydroxytryptamine transporter -- Machine learning -- Ensemble model -- Inhibitor; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107303
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The use of 5-HT reuptake inhibitors can improve the condition of depression. SERT inhibitor data from ChEMBL and DrugBank databases are collected and screened. 16 inhibitor classification prediction models are established. 3 different ensemble learning models are established, and VOT_CLF3 performs best. We find 12 molecular structural alerts for inhibitors. 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.