SIGMAP: an explainable artificial intelligence tool for SIGMA-1 receptor affinity prediction.

ABSTRACT: Developing sigma-1 receptor (S1R) modulators is considered a valuable therapeutic strategy to counteract neurodegeneration, cancer progression, and viral infections, including COVID-19. In this context, in silico tools capable of accurately predicting S1R affinity are highly desirable. Herein, we present a panel of 25 classifiers trained on a curated dataset of high-quality bioactivity data of small molecules, experimentally tested as potential S1R modulators. All data were extracted from ChEMBL v33, and the models were built using five different fingerprints and machine-learning algorithms. Remarkably, most of the developed classifiers demonstrated good predictive performance. The best-performing model, which achieved an AUC of 0.90, was developed using the support vector machine a

SUBMITTER: Lomuscio MC 

PROVIDER: S-EPMC11605305 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature