In silico prediction of GLP-1R agonists using machine learning approach
Chemical Papers, ISSN: 1336-9075, Vol: 75, Issue: 7, Page: 3587-3598
2021
- 1Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Glucagon-like peptide 1 receptor (GLP-1R) is a well-known drug target for the treatment of type 2 diabetes mellitus (T2DM). However, the currently marketed peptidyl GLP-1R agonist drugs are restricted by the requirement of injection. Hence, there is a continued need to develop orally bioavailable small molecule GLP-1R agonist drugs that could be beneficial for the treatment of T2DM. In this study, we report a new strategy to predict small molecule GLP-1R agonists with machine learning approach. Several regression and classification models were built based on support vector machine algorithm and diverse compounds with molecular properties and structural fingerprints as descriptors. For regression models, the ten-fold cross-validation squared correlation coefficient (q, for training sets) and determination coefficient (r, for test sets) of the optimized models were greater than 0.6, respectively. For classification models, the overall predictive accuracies were around or over 90% (for test sets). The results demonstrated that these reliable models could be used to identify highly active agonists for the purpose of virtual screening. The important properties and structural fragments for GLP-1R agonists derived from these models can be used for the novel GLP-1R agonist scaffold design.
Bibliographic Details
Springer Science and Business Media LLC
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