BioTransformer4.0 a comprehensive computational tool for small molecule metabolism prediction
Tian, S.; Feunang, Y. D.; Oler, E.; Wang, F.; Greiner, R.; Palm, E. H.; Schymanski, E. L.; Wishart, D. S.
Show abstract
BioTransformer 4.0, the successor to BioTransformer 3.0, is a freely available in silico metabolism prediction tool. It integrates both knowledge-based and machine learning approaches to predict metabolites for small molecules using one of seven modules: abiotic, environmental, CYP450, phase II, enzyme commission-based, human gut microbial, and all human metabolism. It also provides a customizable sequence prediction module that allows users to simulate multi-step metabolic transformations by chaining among the first six different modules. BioTransformer 4.0 can make predictions more efficiently and accurately than the previous version, as it includes more than 130 new reaction rules, and also an optional validation module to improve the efficiency by restricting the number of predicted metabolites, due to their similarity among real human metabolites. We evaluated its performance by running the six-step all-human metabolism prediction on the DrugBank dataset of 2,457 known biotransformations, and the PhytoHub dataset of 633 known biotransformations - achieving recall values of 87.2% (resp., 91.6%) for the DrugBank (resp., PhytoHub) datasets.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Hierarchical Harmonization of Atom-Resolved Metabolic Re-actions Across Metabolic Databases 97%
- Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases 97%
- Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.