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

Ya Chen, Thomas Seidel, Roxane Axel Jacob, Steffen Hirte, Angelica Mazzolari, Alessandro Pedretti, Giulio Vistoli, Thierry Langer, Filip Miljković, Johannes Kirchmair (Korresp. Autor*in)

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

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-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

OriginalspracheEnglisch
Seiten (von - bis)348-358
Seitenumfang11
FachzeitschriftJournal of Chemical Information and Modeling
Jahrgang64
Ausgabenummer2
Frühes Online-Datum3 Jan. 2024
DOIs
PublikationsstatusVeröffentlicht - 22 Jan. 2024

ÖFOS 2012

  • 301207 Pharmazeutische Chemie

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