Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models.

ABSTRACT: Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (M

SUBMITTER: Bi M 

PROVIDER: S-EPMC8954712 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature