Comprehensively-Curated Dataset of CYP450 Interactions: Enhancing Predictive Models for Drug Metabolism

We collected and organized a detailed dataset encompassing both substrates and non-substrates for six principal cytochrome P450 (CYP450) isozymes, responsible for 90\% of Phase I drug metabolism in humans. These isozymes, specifically CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4, play critical roles in the detoxification and metabolic processing of therapeutic compounds. The dataset, meticulously assembled, includes interactions with approximately 2000 compounds per enzyme, ensuring comprehensive coverage and high accuracy. Employing a combination of conventional machine learning techniques alongside advanced methodologies such as Graph Convolutional Networks (GCN), robust models have been developed to elucidate these drug-enzyme interactions. The dataset is poised to significantly contribute to fields requiring pharmacokinetic modeling, furthering drug development efforts and toxicological studies by providing an essential resource for the accurate prediction of metabolic pathways, thereby enhancing drug safety and efficacy assessments.Each CSV file contains four columns:Chemical nameSMILES notationLabels (where 1 indicates a substrate of CYP450 enzymes, and 0 indicates a non-substrate)Data sources

本研究收集并整理了一份详细数据集，涵盖6种主要细胞色素P450 (cytochrome P450)同工酶的底物与非底物数据。该类同工酶负责人体内90%的I相药物代谢反应，具体包括CYP1A2、CYP2C9、CYP2C19、CYP2D6、CYP2E1及CYP3A4，在治疗性化合物的解毒与代谢过程中发挥关键作用。本数据集经精心构建，每种酶对应约2000种化合物的相互作用数据，确保了覆盖范围的全面性与数据的高准确性。研究团队结合传统机器学习技术与图卷积网络 (Graph Convolutional Networks, GCN)等先进方法，开发了稳健的模型以阐释此类药物-酶相互作用机制。本数据集有望为药代动力学建模相关领域提供重要支撑，通过提供可精准预测代谢通路的核心资源，助力药物开发与毒理学研究，进而提升药物安全性与有效性评估水平。每个CSV文件均包含四列：化学名称、SMILES符号、标签（其中1代表该化合物为CYP450酶的底物，0代表为非底物）、数据来源。

搜集汇总

背景与挑战

背景概述

该数据集是一个全面整理的CYP450酶相互作用数据集，涵盖六种主要同工酶约2000种化合物的底物和非底物信息，用于支持药物代谢预测模型的开发。数据以CSV格式提供，包含化学名称、SMILES表示法、标签和数据来源，适用于药代动力学建模和药物开发研究。