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Automating Predictive Toxicology Using ComptoxAI
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AI
DOI：10.1021/acs.chemrestox.2c00074.png)
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摘要
En 中文
ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph structured knowledge base in the context of three real-world use cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The shortest path module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the expand network module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.
Keyword:
ARYL-HYDROCARBON RECEPTOR
PERFLUOROOCTANOIC ACID
DIOXIN
WORKERS
AHR
期刊
IF:
3.8
论文数:
6.6K
被引数:
1.4W
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引用论文
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