Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches

Sigma (σ) receptors (SRs) have emerged as important therapeutic targets due to their roles in various biological pathways. They are classified into two subtypes: S1R, primarily distributed in the central nervous system and related to neuroprotection and neurodegenerative diseases, and S2R mainly expressed in cancer cells and associated with cell proliferation and apoptosis, as well as in neurons. Although S1R and S2R exhibit structural differences in receptor architecture and assembly, they share similar binding site features and ligand recognition mechanisms. This similarity underscores the importance of identifying selective ligands for therapeutic design, especially given the distinct physiological functions of these receptors. In this project, we developed three distinct machine learning (ML) approaches based on classification, regression, and multiclassification models to predict the activity and selectivity profiles of SR ligands. High-quality data sets were curated from public and in-house source; in turn, the data sets were systematically organized and processed for each workflow. Models were built using molecular descriptors and fingerprints, including Mordred, RDKit, ECFP4, ECFP6, and MACCS keys, and trained with various ML algorithms such as extra trees, random forest, support vector machine, k-nearest neighbors, and XGBoost. Rigorous nested and classical 5-fold cross-validation protocols were applied for model selection and validation. At the end, identification of the best workflow was performed by an external validation procedure. Among the workflows, the one-step multiclassification approach, based on extra trees combined with Mordred descriptors, showed the best predictive performance in external validation, offering a robust tool for the identification of selective S1R and S2R ligands.