Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

Öeren, Mario; Kaempf, Sylvia C.; Ponting, David J.; Hunt, Peter A.; Segall, Matthew D.

doi:10.1021/acs.jcim.3c00275

Cited by 5 publications

(2 citation statements)

References 63 publications

(177 reference statements)

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“…Once the (likely) SoMs in a molecule are identified, medicinal chemists can often devise strategies for optimizing the metabolic properties while maintaining the compound’s bioactivity on the biomacromolecular target. Likewise, some metabolite structure predictors (including GLORYx, Meteor, − and XenoNet , ) use predicted SoMs to filter and rank predicted metabolites.…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

Seidel,

Jacob

et al. 2024

J. Chem. Inf. Model.

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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 machinelearning 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 timeconsuming. 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.

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Section: Introductionmentioning

confidence: 99%

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

Chen,

Seidel,

Jacob

et al. 2024

J. Chem. Inf. Model.

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“…In a bid to mitigate these limitations, researchers have explored the possibility of enhancing metabolite structure prediction by using predicted SoMs as a means for filtering and ranking predicted metabolites. Metabolite structure predictors making use of predicted SoMs include GLORYx, 12 Meteor, [13][14][15] and XenoNet. 16,17 https://doi.org/10.26434/chemrxiv-2023-4dnf1 ORCID: https://orcid.org/0000-0003-2667-5877 Content not peer-reviewed by ChemRxiv.…”

Section: Introductionmentioning

confidence: 99%

FAME.AL: Site-of-metabolism prediction with active learning

Chen,

Seidel,

Jacob

et al. 2023

Preprint

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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 the further development of these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. In this context, active learning strategies could yield higher data efficiency and, in addition, provide guidance to experimentalists on which atom environments to investigate next for maximum information gain. Here we report on the development and validation of FAME.AL, an active learning approach for site-of-metabolism prediction that builds on the previously published FAst MEtabolizer (FAME 3). The active learning approach yielded competitive performance for phase 1 and phase 2 metabolism (Matthews correlation coefficients of approximately 0.50 on holdout data) while using only 20% of the training data used by classical modeling setups. Besides high performance and high data efficiency, the active learning approach is also characterized by high robustness and speed. The approach is largely invariant to starting conditions and parameters, and substantial speed-ups can be yielded by using small atom batches rather than individual atoms during the iterative model-building process. The source code of FAME.AL is publicly available.

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Sulfotransferase‐mediated phase II drug metabolism prediction of substrates and sites using accessibility and reactivity‐based algorithms

Vyas,

Das,

Suryanarayana Murty

et al. 2024

Molecular Informatics

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Sulphotransferases (SULTs) are a major phase II metabolic enzyme class contributing ~20 % to the Phase II metabolism of FDA‐approved drugs. Ignoring the potential for SULT‐mediated metabolism leaves a strong potential for drug‐drug interactions, often causing late‐stage drug discovery failures or black‐boxed warnings on FDA labels. The existing models use only accessibility descriptors and machine learning (ML) methods for class and site of sulfonation (SOS) predictions for SULT. In this study, a variety of accessibility, reactivity, and hybrid models and algorithms have been developed to make accurate substrate and SOS predictions. Unlike the literature models, reactivity parameters for the aliphatic or aromatic hydroxyl groups (R/Ar−O−H), the Bond Dissociation Energy (BDE) gave accurate models with a True Positive Rate (TPR)=0.84 for SOS predictions. We offer mechanistic insights to explain these novel findings that are not recognized in the literature. The accessibility parameters like the ratio of Chemgauss4 Score (CGS) and Molecular Weight (MW) CGS/MW and distance from cofactor (Dis) were essential for class predictions and showed TPR=0.72. Substrates consistently had lower BDE, Dis, and CGS/MW than non‐substrates. Hybrid models also performed acceptablely for SOS predictions. Using the best models, Algorithms gave an acceptable performance in class prediction: TPR=0.62, False Positive Rate (FPR)=0.24, Balanced accuracy (BA)=0.69, and SOS prediction: TPR=0.98, FPR=0.60, and BA=0.69. A rule‐based method was added to improve the predictive performance, which improved the algorithm TPR, FPR, and BA. Validation using an external dataset of drug‐like compounds gave class prediction: TPR=0.67, FPR=0.00, and SOS prediction: TPR=0.80 and FPR=0.44 for the best Algorithm. Comparisons with standard ML models also show that our algorithm shows higher predictive performance for classification on external datasets. Overall, these models and algorithms (SOS predictor) give accurate substrate class and site (SOS) predictions for SULT‐mediated Phase II metabolism and will be valuable to the drug discovery community in academia and industry. The SOS predictor is freely available for academic/non‐profit research via the GitHub link.

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Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

Cited by 5 publications

References 63 publications

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

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

FAME.AL: Site-of-metabolism prediction with active learning

Sulfotransferase‐mediated phase II drug metabolism prediction of substrates and sites using accessibility and reactivity‐based algorithms

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