%0 Journal Article %1 djoumbou-feunang_biotransformer_2019 %A Djoumbou-Feunang, Yannick %A Fiamoncini, Jarlei %A Gil-de-la Fuente, Alberto %A Greiner, Russell %A Manach, Claudine %A Wishart, David S. %D 2019 %J Journal of Cheminformatics %K Biotransformation, Enzyme-substrate Knowledge-based Machine Mass Metabolic Metabolism Metabolite Microbial Structure-based classification degradation, ecomodelling identification, learning, pathway, prediction, specificity, spectrometry, system, %N 1 %P 2 %R 10.1186/s13321-018-0324-5 %T BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification %U https://doi.org/10.1186/s13321-018-0324-5 %V 11 %X A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.

@article{djoumbou-feunang_biotransformer_2019, abstract = {A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.}, added-at = {2023-07-31T08:05:54.000+0200}, author = {Djoumbou-Feunang, Yannick and Fiamoncini, Jarlei and Gil-de-la Fuente, Alberto and Greiner, Russell and Manach, Claudine and Wishart, David S.}, biburl = {https://www.bibsonomy.org/bibtex/2febb089912045d0f76e256d066e0cdb1/jascal_panetzky}, doi = {10.1186/s13321-018-0324-5}, file = {Full Text PDF:/Users/pascal/Zotero/storage/XR7FBHDY/Djoumbou-Feunang et al. - 2019 - BioTransformer a comprehensive computational tool.pdf:application/pdf;Snapshot:/Users/pascal/Zotero/storage/GXMMLF3C/s13321-018-0324-5.html:text/html}, interhash = {97e9030fa9c31cb05a38be691aabce8f}, intrahash = {febb089912045d0f76e256d066e0cdb1}, issn = {1758-2946}, journal = {Journal of Cheminformatics}, keywords = {Biotransformation, Enzyme-substrate Knowledge-based Machine Mass Metabolic Metabolism Metabolite Microbial Structure-based classification degradation, ecomodelling identification, learning, pathway, prediction, specificity, spectrometry, system,}, month = jan, number = 1, pages = 2, shorttitle = {{BioTransformer}}, timestamp = {2023-07-31T08:07:14.000+0200}, title = {{BioTransformer}: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification}, url = {https://doi.org/10.1186/s13321-018-0324-5}, urldate = {2023-07-07}, volume = 11, year = 2019 }