Explainable machine learning predictions of dual-target compounds reveal characteristic structural features.

ABSTRACT: Compounds with defined multi-target activity play an increasingly important role in drug discovery. Structural features that might be signatures of such compounds have mostly remained elusive thus far. We have explored the potential of explainable machine learning to uncover structural motifs that are characteristic of dual-target compounds. For a pharmacologically relevant target pair-based test system designed for our study, accurate prediction models were derived and the influence of molecular representation features of test compounds was quantified to explain the predictions. The analysis revealed small numbers of specific features whose presence in dual-target and absence in single-target compounds determined accurate predictions. These features formed coherent substructures in dual-t

SUBMITTER: Feldmann C 

PROVIDER: S-EPMC8566526 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature