SVMDLF: A novel R-based Web application for prediction of dipeptidyl peptidase 4 inhibitors

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Abstract

Dipeptidyl peptidase 4 (DPP4) is a well-known target for the antidiabetic drugs. However, currently available DPP4 inhibitor screening assays are costly and labor-intensive. It is important to create a robust in silico method to predict the activity of DPP4 inhibitor for the new lead finding. Here, we introduce an R-based Web application SVMDLF (SVM-based DPP4 Lead Finder) to predict the inhibitor of DPP4, based on support vector machine (SVM) model, predictions of which are confirmed by in vitro biological evaluation. The best model generated by MACCS structure fingerprint gave the Matthews correlation coefficient of 0.87 for the test set and 0.883 for the external test set. We screened Maybridge database consisting approximately 53,000 compounds. For further bioactivity assay, six compounds were shortlisted, and of six hits, three compounds showed significant DPP4 inhibitory activities with IC50 values ranging from 8.01 to 10.73 μM. This application is an OpenCPU server app which is a novel single-page R-based Web application for the DPP4 inhibitor prediction. The SVMDLF is freely available and open to all users at http://svmdlf.net/ocpu/library/dlfsvm/www/ and http://www.cdri.res.in/svmdlf/.

SVMDLF is an R programing language-based Web application to predict the inhibitor of dipeptidyl peptidase 4 as an antidiabetic agent. Known active and putative inactive compounds were used to develop SVM classification model, predictions of which are confirmed by in vitro biological evaluation. The SVMDLF is freely available and open to all users.

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