Design and evaluation of dual c-Met/β-tubulin inhibitors for triple-negative breast cancer: An In Silico approach.

Triple-negative breast cancer (TNBC) remains one of the most aggressive and treatment-resistant subtypes of breast malignancies, defined by the absence of hormone receptors and HER2 amplification. This study introduces an integrated computer-aided drug design (CADD) framework that combines Monte Carlo-optimized QSAR modeling, molecular docking, and in silico pharmacokinetic assessment to identify dual c-Met/β-tubulin inhibitors with potential anti-TNBC activity. Robust conformation-independent QSAR models were developed using SMILES- and graph-based descriptors, achieving high internal consistency (R² > 0.84) and strong external predictivity (Q²ext > 0.77). Statistical validation and the CORAL-based "defect-of-correlation" approach confirmed reliability within the defined applicability domain. Fragment-level interpretation revealed molecular motifs associated with enhanced cytotoxicity, which were subsequently used in the CADD design of new multitarget analogs, enabling rational structure-activity relationship (SAR) refinement. Molecular docking against both c-Met and β-tubulin demonstrated complementary binding orientations and stable non-covalent interactions, supporting the proposed multitarget mechanism. In silico pharmacokinetic analysis indicated favorable drug-likeness, high gastrointestinal absorption, absence of major metabolic liabilities, and low predicted toxicity, suggesting a well-balanced safety-efficacy profile. Collectively, this study establishes a CADD-based multitarget modeling platform that integrates predictive, mechanistic, and pharmacokinetic insights, providing a rational foundation for the development of next-generation personalized therapeutics for TNBC.