bioRxivBioinformaticsPreprint
BioTransformer4.0 a comprehensive computational tool for small molecule metabolism prediction
Siyang TianiD, Yannick Djoumbou-FeunangiD, Eponine Oler, Fei WangiD, Russell GreineriD, Emma Helena PalmiD, Emma SchymanskiiD, Claudine ManachiD, David Scott WishartiD
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.
- organism: human
- assay: metabolomics
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- Citations
- 5
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