Natural Language Processing-Based Comprehensive Data Analysis for Interaction Between Chemicals and Drug Metabolism Network

药物代谢与药物的疗效和安全性密切相关，药物代谢的抑制或诱导是指药物在给药后的相互作用。为了预测药物与药物的相互作用，不仅需要全面了解化学物质与单个药物代谢酶的相互作用，而且需要全面了解化学物质与调节酶的表达和活性的网络系统的相互作用。在这项研究中，我们开发了一种基于自然语言处理的文本挖掘技术，用于系统地收集化学物质与药物代谢相关网络之间相互作用的信息。此外，基于收集到的信息，我们系统地进行了结构-活性关系分析。这些结果将为药物设计和发现提供线索，旨在开发更安全的药物。

Drug metabolism is closely related to efficacy and safety of drugs, of which inhibition or induction is involved in drug-drug interaction following administration of multiple drugs. In order to predict drug-drug interactions, it is required to comprehensively understand not only interaction of chemicals with individual drug-metabolizing enzymes but that with a network system regulating expression and activity of the enzymes. In the present study, we developed a natural language processing-based text-mining technique for systematically collecting information on interaction between chemicals and a drug metabolism-related network. In addition, based on the information collected, we systematically performed structure-activity relationship analyses. These results will present a clue to drug design and discovery intended for development of safer drugs.