Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations.

ABSTRACT: Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize promiscuous compounds and set them apart from compounds with single-target activity. We have devised a test system using machine learning to systematically examine structural features that might characterize compounds with multi-target activity. Using this system, more than 860,000 diagnostic predictions were carried out. The analysis provided compelling evidence for the presence of structural characteristics of promiscuous compounds that were dependent on given target combinations, but not gen

SUBMITTER: Feldmann C 

PROVIDER: S-EPMC8042106 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature