Research Article

QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential

Volume: 2026 Number: 2 September 23, 2026

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

Supporting Institution

Teerthanker Mahaveer College of Pharmacy, Teerthanker Mahaveer University

Ethical Statement

The authors declare that this study did not involve any human participants or experimental animals. All analyses were conducted using computational and in-silico methods based on previously reported data. Therefore, ethical approval was not required for this research. The authors confirm that the manuscript complies with the ethical standards and publication guidelines of the journal.

References

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Details

Primary Language

English

Subjects

Biomolecular Modelling and Design, Cheminformatics and Quantitative Structure-Activity Relationships

Journal Section

Research Article

Publication Date

September 23, 2026

Submission Date

January 26, 2026

Acceptance Date

May 4, 2026

Published in Issue

Year 2026 Volume: 2026 Number: 2

APA
Yadav, R. K., Chandra, P., & Patil, V. (2026). QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential. Journal of the Turkish Chemical Society Section A: Chemistry, 2026(2), 1-21. https://doi.org/10.18596/jotcsa.1871898
AMA
1.Yadav RK, Chandra P, Patil V. QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential. JOTCSA. 2026;2026(2):1-21. doi:10.18596/jotcsa.1871898
Chicago
Yadav, Rakesh Kumar, Phool Chandra, and Vaishali Patil. 2026. “QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors With Anticancer Potential”. Journal of the Turkish Chemical Society Section A: Chemistry 2026 (2): 1-21. https://doi.org/10.18596/jotcsa.1871898.
EndNote
Yadav RK, Chandra P, Patil V (September 1, 2026) QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential. Journal of the Turkish Chemical Society Section A: Chemistry 2026 2 1–21.
IEEE
[1]R. K. Yadav, P. Chandra, and V. Patil, “QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential”, JOTCSA, vol. 2026, no. 2, pp. 1–21, Sept. 2026, doi: 10.18596/jotcsa.1871898.
ISNAD
Yadav, Rakesh Kumar - Chandra, Phool - Patil, Vaishali. “QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors With Anticancer Potential”. Journal of the Turkish Chemical Society Section A: Chemistry 2026/2 (September 1, 2026): 1-21. https://doi.org/10.18596/jotcsa.1871898.
JAMA
1.Yadav RK, Chandra P, Patil V. QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential. JOTCSA. 2026;2026:1–21.
MLA
Yadav, Rakesh Kumar, et al. “QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors With Anticancer Potential”. Journal of the Turkish Chemical Society Section A: Chemistry, vol. 2026, no. 2, Sept. 2026, pp. 1-21, doi:10.18596/jotcsa.1871898.
Vancouver
1.Rakesh Kumar Yadav, Phool Chandra, Vaishali Patil. QSAR-Based Predictive Modeling and Mechanistic Insights into MMP-2 and MMP-9 Inhibitors with Anticancer Potential. JOTCSA. 2026 Sep. 1;2026(2):1-21. doi:10.18596/jotcsa.1871898