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Predicting DPP-IV inhibitors with machine learning approaches

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Abstract

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.

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Acknowledgements

This work was supported by the National Science Foundation of China (81473138, 81573310), Guangdong Province Science and Technology Planning Project (2016A020217002), Guangdong Province Frontier and Key Technology Innovation Program (2015B010109004), Guangdong National Science Foundation (2016A030310228) and Guangdong NSF (2016A030310228). We also thank Professor Johann Gasteiger for his advice and proof-reading the manuscript.

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Authors and Affiliations

  1. Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China

    Jie Cai, Chanjuan Li, Zhihong Liu, Jiewen Du, Qiong Gu & Jun Xu

  2. Lipid Biology and Metabolic Disease Health Innovations Research Institute, RMIT University, PO Box 71, Melbourne, VIC, 3083, Australia

    Jiming Ye

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  1. Jie Cai
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  2. Chanjuan Li
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  3. Zhihong Liu
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Correspondence to Jun Xu.

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Cai, J., Li, C., Liu, Z. et al. Predicting DPP-IV inhibitors with machine learning approaches. J Comput Aided Mol Des 31, 393–402 (2017). https://doi.org/10.1007/s10822-017-0009-6

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  • DOI: https://doi.org/10.1007/s10822-017-0009-6

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