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2000
Volume 21, Issue 2
  • ISSN: 1573-4080
  • E-ISSN: 1875-6662

Abstract

Introduction

Breast cancer is a leading cause of death among women worldwide. The limitations of current therapies, drug resistance, and toxicity emphasize the urgent need for targeted therapeutic agents. The 4H-chromen-4-one scaffold has emerged as a promising framework for developing potent anticancer agents, particularly against MCF-7 breast cancer cells. This study investigates the anticancer potential of 4H-chromen-4-one derivatives against the MCF-7 breast cancer cell line using computational modelling.

Methods

QSAR models were constructed using PaDEL molecular descriptors using conventional QSARINS software and state-of-the-art machine learning techniques.

Results

The QSAR model through QSARINS demonstrated strong predictive performance with a leave-one-out cross-validated R2 (Q2loo) value of 0.7790 (internal validation) and a squared correlation coefficient of the test set (R2ext) value of 0.5049 (external validation). Whereas, machine learning models yielded R2 values of 0.6614, 0.9404, 0.9093, 0.9093, and 0.714 for random forest, support vector regression, multiple linear regressions, partial least squares, and ordinary least squares, respectively.

Discussion

Key molecular descriptors contributing significantly to the model included MATS2e (electronic descriptor), SCH-5 (topological descriptor), and minaasC (electronic descriptor), indicating their importance in breast cancer activity. The predicted pIC values for the 4H-chromen-4-one derivatives from QSARINS and machine learning techniques fit well within the chemical space, further validating the models.

Conclusion

This study highlights the potential of 4H-chromen-4-one derivatives as leads for anticancer drug development. The identified electronic descriptors are essential for their activity and can guide structural optimization. Further and studies are warranted to validate their therapeutic potential.

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2025-09-28

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Discovering a 4H-chromen-4-one Framework to Target Breast Cancer with a State-of-the-art QSAR Model
Curr. Enzym. Inhib. 21, 153 (2025); https://doi.org/10.2174/0115734080335471250320032842
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  • Article Type:     Research Article
Keyword(s): 4H-chromen-4-one; Breast cancer; computational modeling; drug discovery; machine learning; MCF-7 cells; molecular descriptors; QSAR; QSARINS software

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