An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

ABSTRACT: The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized

SUBMITTER: Lin TE 

PROVIDER: S-EPMC11081523 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature