This journal focuses on the application of computational methods, including modeling and machine learning, to understand and predict the toxicological effects of chemical substances. Research explores the development and validation of quantitative models for predicting chemical exposure, internal dose, and biological responses across various biological systems and exposure scenarios. The journal also investigates the use of in silico approaches for chemical safety assessment and risk evaluation.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Computer Science ApplicationsQ2
Health, Toxicology and MutagenesisQ2
ToxicologyQ2
Subject Classification
Web of Science Categories
Toxicology
Scopus Categories
Computer Science ApplicationsToxicologyHealth, Toxicology and Mutagenesis
Research Topics (OpenAlex)
Computational Drug Discovery MethodsAnimal testing and alternativesEffects and risks of endocrine disrupting chemicalsCarcinogens and Genotoxicity AssessmentChemistry and Chemical EngineeringMetabolomics and Mass Spectrometry StudiesPesticide Exposure and ToxicityPharmacogenetics and Drug MetabolismBioinformatics and Genomic NetworksAnalytical Chemistry and Chromatography