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 exp...

Full description

Saved in:

Bibliographic Details
Main Author:	Lisa Lombardo (7301969) (author)
Other Authors:	Verena Battisti (8581839) (author), Thierry Langer (1316790) (author), Rosaria Gitto (2013064) (author), Laura De Luca (8152170) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Molecular Biology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified various biological pathways support vector machine ligand recognition mechanisms distinct physiological functions central nervous system >- nearest neighbors step multiclassification approach s2r mainly expressed best predictive performance various ml algorithms quality data sets extra trees combined identifying selective ligands external validation procedure extra trees data sets s2r ligands external validation sr ligands selective s1r validation protocols two subtypes therapeutic design systematically organized similarity underscores selectivity profiles robust tool rigorous nested receptor architecture random forest primarily distributed neurodegenerative diseases multiclassification models mordred descriptors model selection maccs keys k </ including mordred house source fold cross especially given classical 5 cell proliferation cancer cells best workflow
Tags:	Add Tag No Tags, Be the first to tag this record!

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

Similar Items