TY - JOUR
T1 - Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation
AU - Chen, Ya
AU - Seidel, Thomas
AU - Jacob, Roxane Axel
AU - Hirte, Steffen
AU - Mazzolari, Angelica
AU - Pedretti, Alessandro
AU - Vistoli, Giulio
AU - Langer, Thierry
AU - Miljković, Filip
AU - Kirchmair, Johannes
N1 - Publisher Copyright:
© 2024 The Authors. Published by American Chemical Society
PY - 2024/1/22
Y1 - 2024/1/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85182011161&partnerID=8YFLogxK
U2 - 10.1021/acs.jcim.3c01588
DO - 10.1021/acs.jcim.3c01588
M3 - Article
C2 - 38170877
VL - 64
SP - 348
EP - 358
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
SN - 1549-9596
IS - 2
ER -