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. To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found. BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/ . Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca , which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

中文翻译：

---

BioTransformer：用于小分子代谢预测和代谢物鉴定的综合计算工具。

在过去的20年中，已经开发了许多用于代谢预测的计算工具，以预测经历生物转化或环境降解的小分子的结构。这些工具的开发主要是为了促进吸收，分布，代谢，排泄和毒性（ADMET）研究，尽管现在人们对使用此类工具促进代谢组学和暴露组学研究越来越感兴趣。但是，它们的使用和广泛采用仍然受到几个因素的阻碍，包括它们的范围有限，覆盖范围，可用性和性能。为了解决这些局限性，我们开发了BioTransformer，这是一个免费提供的软件包，用于准确，快速而全面地进行计算机代谢预测和化合物鉴定。BioTransformer通过其代谢预测工具将机器学习方法与基于知识的方法相结合，以预测人体组织（例如肝组织），人体肠道以及环境（土壤和水的微生物群）中的小分子代谢。对BioTransformer的全面评估表明，它能够胜过两个最先进的商用工具（Meteor Nexus和ADMET Predictor），其精确度和召回值比Meteor Nexus或ADMET Predictor的要高7倍。在相同或相同的限制条件下，对同一套药物，农药，植物化学药品或内生生物素使用。此外，BioTransformer能够重现EAWAG途径预测系统预测的100％转化和代谢产物。利用从补充表儿茶素的大鼠实验研究中获得的质谱数据，BioTransformer还能够通过其代谢识别工具正确鉴定39种先前报道的表儿茶素代谢物，并提出28种潜在的代谢物，其中17种与9种单同位素物质相匹配，没有证据表明可以找到以前的报告。BioTransformer可用作开放访问命令行工具或软件库。可从https://bitbucket.org/djoumbou/biotransformerjar/免费获得。此外，它还可以在www.biotransformer.ca上作为开放式RESTful应用程序免费获得，该应用程序允许用户手动或以编程方式提交查询，