Curated CYP450 Interaction Dataset: Covering the Majority of Phase I 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.

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Publication Details

Journal
Scientific Data
Published
2025-08-14
DOI
https://doi.org/10.1038/s41597-025-05753-8
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50

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Curated CYP450 Interaction Dataset: Covering the Majority of Phase I Drug Metabolism

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article

Curated CYP450 Interaction Dataset: Covering the Majority of Phase I Drug Metabolism

Tien-Chueh Kuo, Shangchen Yang, Yufeng Jane Tseng, Yu-Wen Su, Peng Du, Jon‐Chao Hong, Yu‐Ting Hsu, Yuhao Ni
article en
1 citations

Abstract

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.

Scientific DataVol. 12(1)
National Taiwan University (TW), National Taipei University (TW)
Ministry of Education, India, National Taiwan University, National Science and Technology Council
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
1.50
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