BreastCAR: A Machine Learning Framework for Breast Cancer Drug Activity Prediction

Breast cancer is a highly prevalent and molecularly heterogeneous malignancy, where therapeutic resistance and off‐target toxicity remain major challenges. Here, we introduce BreastCAR, a multi‐target quantitative structure–activity relationship framework trained on ChEMBL bioactivity data covering 53 cancer‐relevant targets and 233975 compounds. The model integrates 2048‐bit Morgan fingerprints with physicochemical descriptors and applies random forest, XGBoost, LightGBM (LGB), support vector machine with RBF kernel (SVM‐RBF) and multilayer perceptron (MLP) for both regression (pIC ₅₀ ) and three‐class activity classification. The framework achieves a mean R ² of 0.723 and root mean squared error of 0.661, with a peak R ² of 0.894 for NOTC1. Classification performance shows a mean F1 score of 0.826, area under the curve–receiver operating characteristic of 0.919, Matthews correlation coefficient of 0.679 and balanced accuracy of 0.759. Y‐randomisation confirmed non‐trivial model learning (permuted R ²  = −0.167 vs. real 0.723; permuted F1 = 0.467 vs. real 0.826). Applicability domain analysis (Tanimoto k‐NN, 95th‐percentile threshold) demonstrated that 94.8% of test compounds fall within the training AD. To experimentally validate the top‐ranked BreastCAR predictions, a panel of 13 benzylidene‐scaffold compounds (R26‐01 to R26‐13) was evaluated in vitro against MCF‐7 human breast cancer cells using the MTT antiproliferative assay ( n  = 3, IC ₅₀ determined by non‐linear regression). Two compounds—R26‐06 (4‐bromobenzylidene, IC ₅₀  = 5.84 ± 0.24 μM, SI = 9.2) and R26‐07 (3‐chlorobenzylidene, IC ₅₀  = 6.72 ± 0.28 μM, SI = 8.4)—surpassed tamoxifen (IC ₅₀  = 14.23 ± 0.58 μM) with statistical significance ( p  < 0.001), demonstrating that BreastCAR‐guided prioritisation translates computational predictions into experimentally confirmed antiproliferative activity. A FastAPI‐based web interface enables real‐time SMILES input, structural visualisation and activity prediction. The complete code and models are provided to ensure reproducibility.