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.