Leveraging multiple data types for improved compound-kinase bioactivity prediction

Bibliographic Details
Title:	Leveraging multiple data types for improved compound-kinase bioactivity prediction
Authors:	Ryan Theisen, Tianduanyi Wang, Balaguru Ravikumar, Rayees Rahman, Anna Cichońska
Source:	Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Publisher Information:	Nature Portfolio, 2024.
Publication Year:	2024
Collection:	LCC:Science
Subject Terms:	Science
More Details:	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 on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2041-1723
Relation:	https://doaj.org/toc/2041-1723
DOI:	10.1038/s41467-024-52055-5
Access URL:	https://doaj.org/article/4d18f6b76b8e4d209b0b977e65748d7a
Accession Number:	edsdoj.4d18f6b76b8e4d209b0b977e65748d7a
Database:	Directory of Open Access Journals

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