Innovations in QSAR for Accelerated Development of Selective Anti-Breast Cancer Molecules

Abstract

Quantitative structure–activity relationship (QSAR) modeling remains a cornerstone of ligand-based drug design. Recent methodological innovations — including deep learning, graph neural networks, multi-task models, transfer learning, and integration with structure-based methods — have increased QSAR’s power to predict and prioritize anti-breast cancer compounds. This review synthesizes modern QSAR developments relevant to breast cancer drug discovery, examines datasets and validation best practices, highlights representative case studies where QSAR accelerated identification of selective inhibitors, and discusses persisting challenges and future directions.  Key themes include descriptor design and representation learning, multi-target and context-aware models for tumor subtypes, interpretability and model uncertainty, and practical integration with ADMET prediction and experimental pipelines. (Keywords: QSAR, deep learning, breast cancer, 3D-QSAR, GNN, ADMET, ChEMBL).