QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential
Abstract
Matrix metalloproteinases MMP-2 and MMP-9 (gelatinase-A and gelatinase-B) play a crucial role in cancer progression through extracellular matrix degradation, tumor invasion, and metastasis,
making them important targets for inhibitor design. In this study, quantitative structure–activity relationship (QSAR) modeling was performed on sulfonyl hydroxamate derivatives to identify structural features governing inhibitory activity. The dataset was divided into training and test sets, and multiple linear regression analysis was employed to derive predictive models. For MMP-2 inhibition, the developed models exhibited strong statistical significance (r = 0.954-0.958, F = 48.0-61.4, p <0.0001), with reliable internal validation (Q2(r2 cv (LOO)) = 0.813-0.860) and moderate external predictivity (r2 pred = 0.708). Similarly, for MMP-9, demonstrated satisfactory performance (r = 0.888-0.963, F = 33.6-54.2, p <0.0001, (Q2(r2 cv (LOO)) = 0.728-0.871 and r2 pred = 0.633). External validation further supported the predictive consistency of the models. Model reliability was confirmed through Y-randomization tests, which produced significantly reduced R² and Q² values for randomized datasets, along with high c Rp 2 values (>0.8), indicating absence of chance correlation. The applicability domain, assessed using Williams plots, showed that most compounds fall
within acceptable limits. Variance Inflation Factor (VIF) analysis indicated multicollinearity among certain descriptors, attributable to intrinsic physicochemical relationships without compromising model stability. Descriptor analysis revealed positive contributions from steric and surface-related parameters, while polarizability showed a negative influence. The models were further applied to predict the activity of newly designed compounds, which exhibited favorable profiles. Despite limitations such as moderate external predictivity, the study provides useful insights into structure-activity relationships and supports the rational design of potential MMP inhibitors with prospective anticancer relevance.
Keywords
- Quantitative Structure–Activity Relationship (QSAR)
- MMP-2
- MMP-9
- Gelatinase Inhibitors
- Anticancer Drug Design
- Molecular Descriptors
Supporting Institution
Ethical Statement
References
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Details
Primary Language
English
Subjects
Biomolecular Modelling and Design, Cheminformatics and Quantitative Structure-Activity Relationships
Journal Section
Research Article
Authors
Publication Date
September 23, 2026
Submission Date
January 26, 2026
Acceptance Date
May 4, 2026
Published in Issue
Year 2026 Volume: 2026 Number: 2
