Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds. (December 2021)

• Record Type: Journal Article
• Authors: Hermansyah, Oky; Bustamam, Alhadi; Yanuar, Arry
• Is Part Of: Computational biology and chemistry. Volume 95(2021)
• Publication Date: 2021-12
• Subjects: DL Deep Learning -- XGBoost XGBoost Tree Ensemble -- RF Random Forest -- MLR Multiple Linear Regression -- SVR Support Vector Regression -- SVM Support Vector Machine; Artificial intelligence -- DPP-4 -- KNIME -- Machine learning -- QSAR -- Virtual screening; 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.2021.107597
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R 2 pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC50 value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets. Graphical Abstract: ga1 Highlights: 5 models of machine learning were used to build a QSAR model of DPP-4 inhibitor: DL, XGBoost, RF, MLR, and SVR. The SVR model provided the best performance that meets the QSAR parametric standard value for pIC50 prediction of DPP-4 inhibitor. From the results of virtual screening and molecular docking, CH0002 showed inhibitory selectivity against DPP4.