Abstract
Machine learning models were developed using affinity screening data and applied to virtually screen commercially available compounds. The top-ranked compounds from this virtual screening were then tested experimentally to determine their inhibitory potency against the soluble epoxide hydrolase (sEH) target. This dataset provides (1) the measured IC50 values for 2002 compounds evaluated in dose-response assays with sEH after selection through the machine learning models. (2) cherry-picked 62 compounds to re-test using higher concentration.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Xu, Jin, "IC50 assay data again the sEH protein target using prediction from Machine Learning Applied to Affinity Screening Data", Technical Disclosure Commons, (October 05, 2023)
https://www.tdcommons.org/dpubs_series/6300