HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model

Liver toxicity poses a critical challenge in drug development due to the liver's pivotal role in drug metabolism and detoxification. Accurately predicting liver toxicity is crucial but is hindered by scattered information sources, a lack of curation standards, and the heterogeneity of data pers...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of cheminformatics Ročník 17; číslo 1; s. 48 - 14
Hlavní autoři: Han, Jiyeon, Zhung, Wonho, Jang, Insoo, Lee, Joongwon, Kang, Min Ji, Lee, Timothy Dain, Kwack, Seung Jun, Kim, Kyu-Bong, Hwang, Daehee, Lee, Byungwook, Kim, Hyung Sik, Kim, Woo Youn, Lee, Sanghyuk
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 08.04.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Accessibility
Biocompatibility
Chemistry
Chemistry and Materials Science
Computational Biology/Bioinformatics
Computer Applications in Chemistry
Deep learning
Detoxification
Documentation and Information in Chemistry
Drug development
Drug induced liver injury
Drug metabolism
Fine-tuning
Foundation model
Graph neural networks
Hepatotoxicity
Heterogeneity
Information sources
Knowledge bases (artificial intelligence)
Liver
Liver toxicity
Machine learning
Molecular graph
Neural networks
Physiological aspects
Prediction models
Theoretical and Computational Chemistry
Toxicity
Timor-Leste
Drug induced liver injury
Deep neural network
Molecular graph
Graph neural network
Liver toxicity
Hepatotoxicity
Fine-tuning
Foundation model
ISSN:1758-2946, 1758-2946
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Liver toxicity poses a critical challenge in drug development due to the liver's pivotal role in drug metabolism and detoxification. Accurately predicting liver toxicity is crucial but is hindered by scattered information sources, a lack of curation standards, and the heterogeneity of data perspectives. To address these challenges, we developed the HepatoToxicity Portal (HTP), which integrates an expert-curated knowledgebase (HTP-KB) and a state-of-the-art machine learning model for toxicity prediction (HTP-Pred). The HTP-KB consolidates hepatotoxicity data from nine major databases, carefully reviewed by hepatotoxicity experts and categorized into three levels: in vitro, in vivo, and clinical, using the Medical Dictionary for Regulatory Activities (MedDRA) terminology. The knowledgebase includes information on 8,306 chemicals. This curated dataset was used to build a hepatotoxicity prediction module by fine-tuning a GNN-based foundation model, which was pre-trained with approximately 10 million chemicals in the PubChem database. Our model demonstrated excellent performance, achieving an area under the ROC curve (AUROC) of 0.761, surpassing existing methods for hepatotoxicity prediction. The HTP is publicly accessible at https://kobic.re.kr/htp/ , offering both curated data and prediction services through an intuitive interface, thus effectively supporting drug development efforts. Scientific contributions HTP-KB consolidates comprehensive curated information on liver toxicity gathered from nine sources. HTP-Pred utilizes advanced deep learning techniques, significantly enhancing predictive accuracy. Together, these tools provide valuable resources for researchers and practitioners in drug development, accessible through a user-friendly interface.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-025-00992-8