ArticleScientific data2025
Curated CYP450 Interaction Dataset: Covering the Majority of Phase I Drug Metabolism.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics.Pharmaceutics · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.