Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
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 g...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/25/24/5942 |
| _version_ | 1827700002577711104 |
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| author | Maciej Przybyłek |
| author_facet | Maciej Przybyłek |
| author_sort | Maciej Przybyłek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:03:18Z |
| format | Article |
| id | doaj.art-c09b829aad0d4aec929a9a7694848625 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T14:03:18Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-c09b829aad0d4aec929a9a76948486252023-11-21T00:55:14ZengMDPI AGMolecules1420-30492020-12-012524594210.3390/molecules25245942Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors ScreeningMaciej Przybyłek0Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, PolandBeta-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.https://www.mdpi.com/1420-3049/25/24/5942beta-glucosidaseenzyme inhibitorsvirtual screening2D molecular descriptorsbinary classificationneural networks |
| spellingShingle | Maciej Przybyłek Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening Molecules beta-glucosidase enzyme inhibitors virtual screening 2D molecular descriptors binary classification neural networks |
| title | Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening |
| title_full | Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening |
| title_fullStr | Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening |
| title_full_unstemmed | Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening |
| title_short | Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening |
| title_sort | application 2d descriptors and artificial neural networks for beta glucosidase inhibitors screening |
| topic | beta-glucosidase enzyme inhibitors virtual screening 2D molecular descriptors binary classification neural networks |
| url | https://www.mdpi.com/1420-3049/25/24/5942 |
| work_keys_str_mv | AT maciejprzybyłek application2ddescriptorsandartificialneuralnetworksforbetaglucosidaseinhibitorsscreening |