Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

TitleChemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.
Publication TypeJournal Article
Year of Publication2018
AuthorsMallory, EK, Acharya, A, Rensi, SE, Turnbaugh, PJ, Bright, RA, Altman, RB
JournalPac Symp Biocomput
Date Published2018

Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

Alternate JournalPac Symp Biocomput
PubMed ID29218869
Grant ListF31 LM012354 / LM / NLM NIH HHS / United States
R01 GM102365 / GM / NIGMS NIH HHS / United States
R01 HL122593 / HL / NHLBI NIH HHS / United States
U01 GM061374 / GM / NIGMS NIH HHS / United States