NO classifier prediction of anti neuroinflammatory agents using text mining of 3D molecular fingerprints.

ABSTRACT: CNS Drug discovery has been challenging due to the lack of clarity on CNS diseases' basic biological and pathological mechanisms. Despite the difficulty, some CNS drugs have been developed based on phenotypic effects. Herein, we propose a phenotype-structure relationship model, which predicts an anti-neuroinflammatory potency based on 3D molecular structures of the phenotype-active or inactive compounds without specifying targets. For this chemo-centric study, a predictive model of the nitric oxide (NO) inhibitory potency in hyper-activated microglia is built from the 548 agents, which were collected from 95 research articles (28 substructures consisting of natural products and synthetic scaffolds) and doubly externally validated by the agents of 9 research articles as third set. 3D Struct

SUBMITTER: Lee SE 

PROVIDER: S-EPMC11569247 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature