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Beyond model interpretability using LDA and decision trees for α‐amylase and α‐glucosidase inhibitor classification studies

2019; Wiley; Volume: 94; Issue: 1 Linguagem: Inglês

10.1111/cbdd.13518

1747-0285

Karel Diéguez‐Santana, Oscar Rivera-Borroto, Amilkar Puris, Hai Pham‐The, Huong Le‐Thi‐Thu, Bakhtiyor Rasulev, Gerardo M. Casañola‐Martín,

Computational Drug Discovery Methods

Abstract In this report are used two data sets involving the main antidiabetic enzyme targets α‐amylase and α‐glucosidase. The prediction of α‐amylase and α‐glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees ( CT ). A large data set of 640 compounds for α‐amylase and 1546 compounds in the case of α‐glucosidase are selected to develop the tree model. In the case of CT ‐J48 have the better classification model performances for both targets with values above 80%–90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10‐fold cross‐validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double‐target approach for increasing the estimation of antidiabetic chemicals identification aimed by double‐way workflow in virtual screening pipelines.