Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

ABSTRACT: The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consu

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10806800 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature