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Vol. 37 No. 10 (2025): Vol 37 Issue 10, 2025

Copyright (c) 2025 Roxzanne Ray, Kaleiaresi Rajan, Vasudeva Rao Avupati

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Machine Learning Models for the Identification of SARS-CoV-2 Main Protease (Mpro) Inhibitors: Development and Validation of Three-dimensional (3D) Quantitative Structure-Activity Relationship (QSAR) and Pharmacophore Models

https://doi.org/10.14233/ajchem.2025.34335
Roxzanne Ray
Department of Pharmaceutical Chemistry, School of Pharmacy, IMU University (Formerly known as International Medical University), 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000 Kuala Lumpur, Malaysia
https://orcid.org/0009-0006-5166-989X
Kaleiaresi Rajan
Department of Pharmaceutical Chemistry, School of Pharmacy, IMU University (Formerly known as International Medical University), 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000 Kuala Lumpur, Malaysia
https://orcid.org/0009-0005-8613-9648
Vasudeva Rao Avupati
Centre of Excellence for Bioactive Molecules & Drug Delivery (BMDD), Institute for Research, Development & Innovation (IRDI), IMU University (Formerly known as International Medical University), 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000 Kuala Lumpur, Malaysia
https://orcid.org/0000-0003-0899-513X

Corresponding Author(s) : Vasudeva Rao Avupati

vasudevaraoavupati@gmail.com

Asian Journal of Chemistry, Vol. 37 No. 10 (2025): Vol 37 Issue 10, 2025

Abstract

The global pandemic of coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent World Health Organization (WHO) statistics show 778 million reported COVID-19 cases. To date, no specific drug has been found to treat COVID-19 effectively, largely due to the emergence of variants of concerns (VOCs). The main protease of SARS-CoV-2 is a well-established drug target to control viral replication in human host. We used a group of chemotypes with experimental Mpro inhibitory properties for the development of atom-based 3D-QSAR and ligand-based 3D-pharmacophore models using advanced machine learning strategies. The established 3D QSAR model is statistically significant (R2Training set = 0.9897, Q2 (R2Test set) = 0.5017), which demonstrated the model’s strong predictive power. The 3D-QSAR model displays contour maps towards the positive and negative contribution of various functional groups based on the active and inactive ligands. On the other hand, we developed a ligand-based, three-point 3D pharmacophore model using 84 ligands (39 actives and 43 inactive) that has demonstrated statistically significant data related to the discrimination of active and inactive groups of compounds with a sensitivity of 97.4%, balanced accuracy of 63.8% and a perfect ROC-AUC of 1.0, internal validation revealed that AAD2 was the best-performing hypothesis. The three-point 3D-pharmacophore model shows fitness and alignment relative to the arrangement of atoms and groups within the active and inactive subsets. In summary, the 3D QSAR and pharmacophore models developed in this study could be used as a virtual screening tool to identify virtual hits as potential SARS-CoV-2 Mpro inhibitors.

Keywords

Machine learning model SARS-CoV-2 Main protease COVID-19 Virtual hits Contour maps Fitness score
(1)
Ray, R.; Rajan, K.; Avupati, V. R. Machine Learning Models for the Identification of SARS-CoV-2 Main Protease (Mpro) Inhibitors: Development and Validation of Three-Dimensional (3D) Quantitative Structure-Activity Relationship (QSAR) and Pharmacophore Models. ajc 2025, 37, 2487-2499.
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