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Workshop: Machine Learning for Molecules

Invited Talk: Yannick Djoumbou Feunang - In Silico Prediction and Identification of Metabolites with BioTransformer

Yannick Djoumbou Feunang



Increased reliance on chemicals in both industrialized and developing countries has led to a dramatic change of our exposure patterns to both natural and synthetic chemicals. This diverse plethora of xenobiotics, some of which have become nearly ubiquitous, includes among others, pesticides, pharmaceuticals, food compounds, and their largely unknown chemo-/biotransformation products. To accurately assess the various environmental health threats they may pose, it is crucial to understand how they are biologically produced, activated, detoxified, and eliminated from various biological matrices. As it turns out, understanding the biological and environmental fate of xenobiotics is a major step towards deciphering the aforementioned mechanisms. Moreover, it contributes significantly to the development of safer and more sustainable chemicals. Over the past decade several in silico tools have been developed for the prediction and identification of metabolites, most of which are only commercially available and significantly biased towards drug-like molecules. In this presentation, we will describe BioTransformer, an open source software and freely accessible server for the prediction of human CYP450-catalyzed metabolism, human gut microbial degradation, human phase-II metabolism, human promiscuous metabolism, and environmental microbial degradation. Moreover, we will present an assessment of its performance in predicting the metabolism of agrochemicals, conducted at Corteva Agriscience. Furthermore, we will illustrate a few examples of its application as demonstrated by various published scientific studies. Finally, we will share future perspectives for this open source project, and describe how it could significantly benefit the exposure science and regulatory communities.


Dr. Yannick Djoumbou Feunang earned his PhD in Microbiology and Biotechnology at the University of Alberta - Canada, in 2017, where his research focused in developing Cheminformatics tools to enhance Metabolomics. Some of his main contributions include software tools ClassyFire, BioTransformer, and CFM-ID 3.0, with applications of ontology and linked data, as well as machine-learning, and knowledge-based artificial intelligence to biology and chemistry. Additionally, he has contributed to the development of databases such as DrugBank and HMDB. Since 2018, Dr. Djoumbou Feunang has worked as a Research Investigator for the Chemistry Data Science research group at Corteva Agriscience in Indianapolis, Indiana. His responsibilities include among others: (1) the development of machine learning models to support lead generation and optimization projects, and; (2) the enhancement of Corteva’s Cheminformatics scientific computing platform. He also currently leads a project aiming at building a cutting-edge, adapted in silico metabolism platform at Corteva Agriscience.