Version 1
: Received: 7 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (11:16:56 CEST)
How to cite:
Ancuceanu, R.; Popovici, P. C.; Drăgănescu, D.; Busnatu, Ș.; Lascu, B. E.; Dinu, M. QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition Based on MACCS Molecular Fingerprints and Virtual Screening of Natural Products. Preprints2024, 2024100529. https://doi.org/10.20944/preprints202410.0529.v1
Ancuceanu, R.; Popovici, P. C.; Drăgănescu, D.; Busnatu, Ș.; Lascu, B. E.; Dinu, M. QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition Based on MACCS Molecular Fingerprints and Virtual Screening of Natural Products. Preprints 2024, 2024100529. https://doi.org/10.20944/preprints202410.0529.v1
Ancuceanu, R.; Popovici, P. C.; Drăgănescu, D.; Busnatu, Ș.; Lascu, B. E.; Dinu, M. QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition Based on MACCS Molecular Fingerprints and Virtual Screening of Natural Products. Preprints2024, 2024100529. https://doi.org/10.20944/preprints202410.0529.v1
APA Style
Ancuceanu, R., Popovici, P. C., Drăgănescu, D., Busnatu, Ș., Lascu, B. E., & Dinu, M. (2024). QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition Based on MACCS Molecular Fingerprints and Virtual Screening of Natural Products. Preprints. https://doi.org/10.20944/preprints202410.0529.v1
Chicago/Turabian Style
Ancuceanu, R., Beatrice Elena Lascu and Mihaela Dinu. 2024 "QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition Based on MACCS Molecular Fingerprints and Virtual Screening of Natural Products" Preprints. https://doi.org/10.20944/preprints202410.0529.v1
Abstract
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, R2 ≥ 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 pIC50 values ≥8 (IC50 values ≤10 nM). Our svm-based ensemble model predicted IC50 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 Iris × germanica 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.
Medicine and Pharmacology, Pharmacology and Toxicology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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