FAME3R: an efficient, practical and reliable open-source tool for predicting phase 1 and phase 2 sites of metabolism

TL;DR

FAME3R is an improved open-source tool for predicting where metabolism occurs in molecules, making it more efficient and easier to use in drug and chemical development.

Contribution

FAME3R introduces a reliability assessment method based on Shannon entropy and supports multiple featurization strategies for metabolism site prediction.

Findings

FAME3R improves upon FAME3 by enhancing maintainability, scalability, and interoperability.

The tool offers a novel reliability assessment method and multiple featurization options.

FAME3R is available as an open-source Python package with flexible interfaces.

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.

The online version contains supplementary material available at 10.1186/s13321-026-01161-1.

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