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Article

Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland
Academic Editor: Alla P. Toropova
Molecules 2020, 25(24), 5942; https://doi.org/10.3390/molecules25245942
Received: 19 November 2020 / Revised: 12 December 2020 / Accepted: 14 December 2020 / Published: 15 December 2020
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models’ accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs. View Full-Text
Keywords: beta-glucosidase; enzyme inhibitors; virtual screening; 2D molecular descriptors; binary classification; neural networks beta-glucosidase; enzyme inhibitors; virtual screening; 2D molecular descriptors; binary classification; neural networks
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MDPI and ACS Style

Przybyłek, M. Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening. Molecules 2020, 25, 5942. https://doi.org/10.3390/molecules25245942

AMA Style

Przybyłek M. Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening. Molecules. 2020; 25(24):5942. https://doi.org/10.3390/molecules25245942

Chicago/Turabian Style

Przybyłek, Maciej. 2020. "Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening" Molecules 25, no. 24: 5942. https://doi.org/10.3390/molecules25245942

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