A (Comprehensive) Review of the Application of Quantitative Structure–Activity Relationship (QSAR) in the Prediction of New Compounds with Anti-Breast Cancer Activity

Computational approaches applied in drug discovery have advanced significantly over the past few decades. These techniques are commonly grouped under the term “computer-aided drug design” (CADD) and are now considered one of the key pillars of pharmaceutical discovery pipelines in both academic and industrial settings. In this work, we review Quantitative Structure–Activity Relationships (QSARs), one of the most used ligand-based drug design (LBDD) methods, with a focus on its application in the discovery and development of anti-breast cancer drugs. Critical steps in the QSAR methodology, essential for its correct application—but often overlooked, leading to insignificant or misleading models—are examined. Additionally, current anti-breast cancer treatment strategies were briefly overviewed, along with some targets for future treatments. The review covers QSAR studies from the past five years and includes a discussion of notable works that could serve as models for future applications of this interdisciplinary and complex method and that may help in feature drug design and development.