Predicting DPP-IV inhibitors with machine learning approaches.
Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV inhibitors are structurally diversified. This makes it difficult to build virtual screening models based upon the known DPP-IV inhibitor libraries using conventional QSAR approaches. In this paper, we report a new strategy to predict DPP-IV inhibitors with machine learning approaches involving naïve Bayesian (NB) and recursive partitioning (RP) methods. We built 247 machine learning models based on 1307 known DPP-IV inhibitors with optimized molecular properties and topological fingerprints as descriptors. The overall predictive accuracies of the optimized models were greater than 80%. An external test set, composed of 65 recently reported compounds, was employed to validate the optimized models. The results demonstrated that both NB and RP models have a good predictive ability based on different combinations of descriptors. Twenty "good" and twenty "bad" structural fragments for DPP-IV inhibitors can also be derived from these models for inspiring the new DPP-IV inhibitor scaffold design.
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
Jie
J
Cai
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
Chanjuan
C
Li
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
Zhihong
Z
Liu
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
Jiewen
J
Du
Lipid Biology and Metabolic Disease Health Innovations Research Institute, RMIT University, PO Box 71, Melbourne, VIC, 3083, Australia.
Jiming
J
Ye
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
Qiong
Q
Gu
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China. junxu@biochemomes.com.
Jun
http://orcid.org/0000-0002-1075-0337
J
Xu
D001499:Bayes Theorem; D017076:Computer-Aided Design; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy; D018819:Dipeptidyl Peptidase 4 / Q000737:chemistry / Q000378:metabolism; D054873:Dipeptidyl-Peptidase IV Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D015195:Drug Design; D006801:Humans; D000069550:Machine Learning; D062105:Molecular Docking Simulation; D054852:Small Molecule Libraries / Q000737:chemistry / Q000494:pharmacology
D054873:Dipeptidyl-Peptidase IV Inhibitors; D054852:Small Molecule Libraries; D018819:Dipeptidyl Peptidase 4
Cheminformatics; DPP-IV; Naïve Bayesian learning; Recursive partitioning; Virtual screening