## 6 Case Studies of QSAR Modeling of Anti-Breast Cancer Agents  
Cancer, including breast cancer, remains one of the most challenging and extensively studied topics in drug design and discovery, attracting significant efforts from numerous scientific groups. This section reviews some of the most significant and recent QSAR studies on anti-breast cancer agents from the past five years. These studies are divided into two groups based on the type of biological activity assessed. The first group includes QSAR models developed using datasets with measured activities against specific targets. These studies are summarized in Table 5, providing additional information on the compounds' scaffolds, their mechanism of action on specific targets, the type of QSAR models based on the descriptors used (2D- or 3D-QSAR), and the software employed. The second group includes studies where activities were evaluated in various cell lines, with exact targets and underlying mechanisms yet to be determined. A similar summary of these studies is presented in Table 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\end{table} 
Table 5: QSAR case studies leveraging activities against various targets. The following sections showcase successful QSAR case studies in which the developed models were applied to design new derivatives that were subsequently synthesized and experimentally validated. This process validates the models and supports their refinement or the development of improved QSAR models. These studies are marked with asterisks in Tables 5 and 6. An outstanding study on 39 selective estrogen receptor (ER) modulators, including partial agonists and antagonists, employed atom-based 3D-QSAR and 3D-pharmacophore searches using PHASE software from [PERSON]'s suite [247]. The dataset selection is noteworthy, as only compounds from co-crystallized structures of complexes with either wild-type or mutated ERs, retrieved from the PDB databank, were considered. The authors built a unique 3D-pharmacophore/3D-QSAR model combining structure-based and ligand-based alignment rules. The derived model was validated using an external test set of 13 compounds and applied to screen 4411 compounds from the National Cancer Institute (NCI) datasets. One of the top predicted compounds, the naturally occurring Brefeldin A (BFA), was identified as showing promising activity against ER\(\alpha\) and the MCF-7 cell line, with notable selectivity against the MDA-MB-231 cell line. Further hit-to-lead structure optimization led to the development of twelve novel BFA derivatives. These compounds underwent in vitro and in vivo tests, demonstrating picomolar to nanomolar potencies against ER\(\alpha\). They were evaluated for their antiproliferative activities as p53 stimulators and as agents for BC cell cycle arrest. The selected leads showed promising anticancer activity, a favorable preclinical profile, and notable safety. Some of these compounds are potential candidates for preclinical and clinical trials, offering a promising future for SERM-related breast cancer therapy. An exemplary study that incorporates several key steps discussed in this review for developing a reliable and robust QSAR model with strong predictive ability focused on