Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates.

ABSTRACT: The development of machine learning models to predict the regioselectivity of C(sp³)-H functionalization reactions is reported. A data set for dioxirane oxidations was curated from the literature and used to generate a model to predict the regioselectivity of C-H oxidation. To assess whether smaller, intentionally designed data sets could provide accuracy on complex targets, a series of acquisition functions were developed to select the most informative molecules for the specific target. Active learning-based acquisition functions that leverage predicted reactivity and model uncertainty were found to outperform those based on molecular and site similarity alone. The use of acquisition functions for data set elaboration significantly reduced the number of data points needed to pe

SUBMITTER: Schleinitz J 

PROVIDER: S-EPMC11887056 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature