QSAR-Based Pharmacophore Optimization of 4-Aminoquinoline Derivatives: Computational Insights into Structure-Activity Relationship and Anticancer Potential

Abstract

Breast cancer is characterised by the uncontrolled growth of cells within the mammary glands, which can later spread to adjacent tissues. The current treatment options include alkylating agents, intercalating agents, topoisomerase inhibitors, antimetabolites, and antimitotic drugs. Computer-aided drug design has contributed in a large way to the development of anticancer drugs. The objective of this study was to perform pharmacophore optimisation of 4- aminoquinoline derivatives using QSARINS software for the development of QSAR models for anticancer activity. A series of thirty-six 4-aminoquinoline derivatives was used to generate 2D and fingerprint-based QSAR models using the Genetic Algorithm-Multiple Linear Regression (GA-MLR) method. The resulting statistical parameters and graphical data were evaluated, and models with strong statistical performance were selected for further analysis. The 2D QSAR model exhibited R² = 0.9757 and Q² = 0.9490, and the fingerprint-based QSAR model displayed values of R² = 0.9884 and Q² = 0.9736. Both models showed internal robustness with limited external predictive capability. The results indicate that electronic and steric factors around the 4-aminoquinoline nucleus strongly influence anticancer activity. The optimised pharmacophore provides a framework for designing new, more potent anticancer agents. The optimised pharmacophore indicates the contribution of electronegative substituents at the 7th position of the quinoline ring, along with a dimethylamino biphenyl group at the 3rd position, for better anticancer activity. Considering the limitations of the present study, namely, limited external predictive capability, further refinement of the models would be required.