ArticleJournal of cheminformatics2026
FAME3R: an efficient, practical and reliable open-source tool for predicting phase 1 and phase 2 sites of metabolism.
Article in Journal of cheminformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Predicting likely sites of metabolism (SOMs), i.e., the atoms in a molecule where metabolic reactions are initiated, is an important component of the computational development pipeline for pharmaceuticals, agrochemicals, and cosmetics. Among SOM prediction tools, FAME3, introduced in 2019, is one of only a few non-commercial models capable of predicting both Phase 1 and Phase 2 SOMs for a wide range of xenobiotics. However, its original implementation posed challenges in maintainability, scalability, and interoperability, which hindered broader adoption. To overcome these limitations, we developed FAME3R, an enhanced version of FAME3 designed to improve computational efficiency and facilitate integration with contemporary cheminformatics workflows. FAME3R introduces several new features, including a novel reliability assessment method based on Shannon entropy and the option to select among various featurization strategies. The tool is available as an open-source Python package, offering both a Python API and a CLI for flexible usage. Additionally, trained FAME3R models can be accessed via a GUI and a REST API hosted on the NERDD web platform.
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