Holistic Evaluation of Biodegradation Pathway Prediction: Assessing Multi-Step Reactions and Intermediate Products

29 March 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental and life sciences. There are several systems that currently predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their accuracy is often evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are related to the nature of the biotransformation experiments. This is particularly true for missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rulebased prediction systems evaluate accuracy only based on the defined set of transformation rules. Therefore, the performance of different models cannot be directly compared.


Machine Learning
biodegradation pathway


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