Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: 'Machine learning methods offer time- and cost-effective means for identifying n ovel chemical matter as well as guiding experimental efforts to map enormous com pound-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-respo nse 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.
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