Leveraging multiple data types for improved compound-kinase bioactivity prediction

Abstract

Machine learning methods offer time- and cost-effective means for identifying novel chemical matter as well as guiding experimental efforts to map enormous compound-kinase interaction spaces. However, considerable challenges for compound-kinase interaction modeling arise from the heterogeneity of available bioactivity readouts, including single-dose compound profiling results, such as percentage inhibition, and multi-dose-response results, such as IC50 . Standard activity prediction approaches utilize only dose-response data in the model training, disregarding a substantial portion of available information contained in single-dose measurements. Here, we propose a novel machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. Our two-stage model first learns a mapping between single-dose and dose-response bioactivity readouts, and then generates proxy dose-response activity labels for compounds that have only been tested in single-dose assays. The predictions from the first-stage model are then integrated with experimentally measured dose-response activities to model compound-kinase binding based on chemical structures and kinase features. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels, particularly in the most practical and challenging scenarios of predicting activities for new compounds and new compound scaffolds. This superior performance is consistent across five evaluated machine learning methods, including traditional models such as random forest and kernel learning, as well as deep learning-based approaches. 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.