Zurück zu den Ergebnissen
Bibliografischer Datensatz · Ansicht und Zugriff
Artículo

Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database

Ankita Dutta et al · BMC · 2026

Bibliografischer Datensatz verfügbar
Schnellübersicht. Prüfen Sie die grundlegenden Angaben und öffnen Sie den Inhalt über die Hauptschaltfläche. Die Seite zeigt nur die Informationen, die zum Identifizieren, Zitieren und Öffnen des Werks nötig sind.

Zugriff auf die Ressource

Öffnen Sie den Inhalt über die Hauptoption oder wählen Sie eine andere verfügbare Quelle.

DOAJ DOAJ Articles
Entrar por DOAJ
Hauptzugriff

Bibliografischer Datensatz verfügbar

El enlace apunta a un catálogo o registro bibliográfico.
Datensatz öffnen

Übersicht

Descripción general del contenido del recurso.

Abstract Humans are exposed to thousands of chemicals throughout their life. Many of these chemicals are detected in blood and have been catalogued in the Blood Exposome Database. Comprehensive hazard assessment of a chemical requires time-consuming and costly lab experiments using animal or cell-lines, which cannot be easily scaled up to the chemical exposome, highlighting the urgent need for computational approaches that can prioritize chemicals based on toxicological information. In this study, we trained direct message passing neural networks (D-MPNN) models using the Chemprop framework, chemical structure, and bioactivity data from 9,581 compounds profiled in theTox21 program across 148 quantitative high-throughput screening assays corresponding to distinct biological endpoints. Additionally, we trained a complementary model using chemical structures (n = 264,601) labeled with known UN-GHS classifications for acute oral toxicity. The Tox21 bioactivity and UN-GHS models demonstrated strong predictive performances, with the 47 bioactivity models and the GHS classification model each achieving AUCs greater than 0.80. We applied these high accuracy models to 52,055 chemicals from the Blood Exposome Database to predict bioactivity and the GHS hazard classification, enabling scalable in-silico prioritization of understudied chemical exposures for further toxicological investigations. Data and code are available at https://zenodo.org/records/17560382 and https://github.com/idslme/exposome-toxicity-prediction . Scientific contributions: We have developed an integrated predictive toxicology workflow for exposomics. This unique workflow utilizes Tox21 endpoints, UN-GHS classification and ADME property prediction models, all implemented in the state-of-the-art Chemprop framework, enabling a ranking of exposome compounds for their toxicity potential. By applying it for the Blood Exposome Database, we have prioritized several chemicals that have been detected in blood samples but can be toxic to human health.

Zitieren

Elegí el formato que necesitás y copiá la referencia al portapapeles.

APA 7

al, A. D. E. (2026). Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database. https://doi.org/10.1186/s13321-026-01187-5

MLA

al, Ankita Dutta et. "Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database." 2026. https://doi.org/10.1186/s13321-026-01187-5.

Chicago

al, Ankita Dutta et. 2026. "Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database.". https://doi.org/10.1186/s13321-026-01187-5.

Harvard

al, A. D. E. 2026, Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database, BMC, available at: https://doi.org/10.1186/s13321-026-01187-5 [Accessed 8 Aug. 2026].

Teilen und drucken

Speichern Sie den Datensatz, kopieren Sie den Permalink oder drucken Sie ihn als PDF.

Referenz exportieren

Exportieren Sie den Datensatz in gängigen Formaten für Literaturverwaltungsprogramme.

Ressourcendetails

Bibliografische Angaben zur Prüfung, ob es sich um das richtige Material handelt.

Titel
Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database
Autor / Mitwirkende
Ankita Dutta et al
Verlag
BMC
Erscheinungsjahr
2026
ISSN
1758-2946
ISSN
1758-2946
Sprache
Inglés

Schlagwörter

Entdecken Sie über diese Schlagwörter weitere verwandte Ressourcen.

Kopiert