Leveraging multiple data types for improved compound-kinase bioactivity prediction.

ABSTRACT: Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, overlooking information contained in single-dose measurements. Here, we propose a machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels. This superior performance is consistent across five diverse machine learning methods. Using the best performing model, we carried out extensive experimental profiling

SUBMITTER: Theisen R 

PROVIDER: S-EPMC11365929 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature