Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

ABSTRACT: Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary

SUBMITTER: Park K 

PROVIDER: S-EPMC6846180 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature