QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition
Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-...
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2024-10-01
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| author | Robert Ancuceanu Patriciu Constantin Popovici Doina Drăgănescu Ștefan Busnatu Beatrice Elena Lascu Mihaela Dinu |
| author_facet | Robert Ancuceanu Patriciu Constantin Popovici Doina Drăgănescu Ștefan Busnatu Beatrice Elena Lascu Mihaela Dinu |
| author_sort | Robert Ancuceanu |
| collection | DOAJ |
| description | Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R<sup>2</sup> ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC<sub>50</sub> values ≥ 8 (IC<sub>50</sub> values ≤ 10 nM). Our svm-based ensemble model predicted IC<sub>50</sub> values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the <i>Iris × germanica</i> rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts. |
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| institution | Kabale University |
| issn | 1424-8247 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Pharmaceuticals |
| spelling | doaj-art-c54eb208f5214379afebf74fe33186332024-11-26T18:17:12ZengMDPI AGPharmaceuticals1424-82472024-10-011711144810.3390/ph17111448QSAR Regression Models for Predicting HMG-CoA Reductase InhibitionRobert Ancuceanu0Patriciu Constantin Popovici1Doina Drăgănescu2Ștefan Busnatu3Beatrice Elena Lascu4Mihaela Dinu5Department of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, RomaniaDepartment of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, RomaniaDepartment of Pharmaceutical Physics, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, RomaniaDepartment of Cardiology, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, RomaniaDepartment of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, RomaniaDepartment of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, RomaniaBackground/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R<sup>2</sup> ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC<sub>50</sub> values ≥ 8 (IC<sub>50</sub> values ≤ 10 nM). Our svm-based ensemble model predicted IC<sub>50</sub> values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the <i>Iris × germanica</i> rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.https://www.mdpi.com/1424-8247/17/11/1448HMG-CoA reductaseQSARstatinsnested cross-validationvirtual screening<i>Iris germanica</i> |
| spellingShingle | Robert Ancuceanu Patriciu Constantin Popovici Doina Drăgănescu Ștefan Busnatu Beatrice Elena Lascu Mihaela Dinu QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition Pharmaceuticals HMG-CoA reductase QSAR statins nested cross-validation virtual screening <i>Iris germanica</i> |
| title | QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition |
| title_full | QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition |
| title_fullStr | QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition |
| title_full_unstemmed | QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition |
| title_short | QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition |
| title_sort | qsar regression models for predicting hmg coa reductase inhibition |
| topic | HMG-CoA reductase QSAR statins nested cross-validation virtual screening <i>Iris germanica</i> |
| url | https://www.mdpi.com/1424-8247/17/11/1448 |
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