Predicting novel targets with Bayesian machine learning by integrating multiple biological signatures.

ABSTRACT: The identification of targets for candidate molecules is a pivotal stride in the drug development journey, encompassing lead discovery, drug repurposing, and the scrutiny of potential off-target or side effects. Consequently, enhancing the precision of target prediction has significant implications. Moreover, current target prediction methods primarily rely on the principle of ligand-based chemical similarity, lacking the capture of novel compound-target relationships based on ligand high-level characterization similarity. Therefore, in this context, we introduce a pioneering algorithm known as the Fused Multiple Biological Signatures (FMBS) strategy. This approach leverages a Bayesian framework to amalgamate 25 predictable biological space characterizations of molecules to predict novel t

SUBMITTER: Wei X 

PROVIDER: S-EPMC11333953 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature