Statistical Learning-Based Prediction of Estrogen Receptor Alpha (ERα) Inhibitor Activities

Estrogen receptor alpha (ERα) is a protein that plays a role in processes such as cell growth and proliferation; however, it has become an important research topic due to its overexpression in 70% of breast cancers. ERα inhibitors stop the growth of cancer cells by blocking the activity of this protein. Traditional drug discovery methods are disadvantageous in terms of time and cost. Various approaches exist in the literature for the discovery of ERα inhibitors. Therefore, a machine learning-based Quantitative Structure-Activity Relationship (QSAR) approach was preferred in this study for the discovery of ERα inhibitors. In this study, a machine learning-based QSAR approach was preferred due to its capacity to extract structure-activity relationships from large chemical datasets and its ability to provide high-throughput screening opportunities. This method enables the prediction of biological activities by expressing the chemical structural properties of molecules through numerical descriptors. The ChEMBL206 target identifier was selected as the data source due to its widespread use, high data quality, and the opportunity for comparison with previous studies. The obtained molecules were classified according to their IC50 values, and their chemical space distributions were analyzed using Lipinski rules. Subsequently, 3153 molecular descriptors were calculated for 3053 molecules using the PADEL program. Feature importance analysis revealed that fingerprints such as PubchemFP667 and PubchemFP527, as well as APC2D atom pair descriptors that stood out in the LightGBM model, played critical roles in ERα inhibition. The developed models demonstrated superior performance with accuracy above 94%, sensitivity around 90%, specificity above 95%, and AUC values above 0.97. This study contributes to the efficiency of the drug discovery process by demonstrating that the activity of ERα inhibitors can be predicted with high accuracy rates.