Computational Toxicology

Aims & Scope

Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.9

Journal Rank

Year	Value
2024	7481

Journal Citation Indicator

Year	Value
2024	513

SJR (SCImago Journal Rank)

Year	Value
2024	0.744

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	28

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Environmental Science and Pharmacology, Toxicology and Pharmaceutics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• EPA’s DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research

  Citation: 142

  Authors: Christopher M., Antony J., Inthirany, Ann M.

• Principles underpinning the use of new methodologies in the risk assessment of cosmetic ingredients

  Citation: 139

  Authors: Matthew, Renata Teixeira, Pedro Amores, Jay, Fanny, Masato, Akihiko, Yutaka, Petra, Reinhard, Stanley, Beta, Julcemara, Andrea, Rob, Eric, Rajeshwar, Nashira Vieira O'Reilly Cabral, Craig, Hajime

• Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making

  Citation: 103

  Authors: A., J.A., E., J.G.M., A., J.L., J.P., M.B., M., T., N.I., G., J., A., J.C., E.A., S., A., R.W., N., J., M., A., Y.M.

• The Adverse Outcome Pathway approach in nanotoxicology

  Citation: 92

  Authors: Kirsten, Brigitte, Andrew, Sharon, Taina, Maurice

• Navigating through the minefield of read-across tools: A review of in silico tools for grouping

  Citation: 91

  Authors: Grace, George, Prachi, Imran

• Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods

  Citation: 88

  Authors: Elisabet, Andrew, Gladys, Alicia, Andrea-Nicole, Frederic Y., Thomas, Sofia, Leo A. van, Andrew, Catherine

• Developing structure-activity relationships for N-nitrosamine activity

  Citation: 77

  Authors: Kevin P., David J.

• Predictive models for acute oral systemic toxicity: A workshop to bridge the gap from research to regulation

  Citation: 72

  Authors: Nicole C., Agnes L., Kamel, David G., Jeremy M., Grace

• Synthesis and characterization of novel thiazole derivatives as potential anticancer agents: Molecular docking and DFT studies

  Citation: 68

  Authors: R., A.M., A.V., K., M.G., S.B., M.K.

• Assessing uncertainty in read-across: Questions to evaluate toxicity predictions based on knowledge gained from case studies

  Citation: 64

  Authors: Terry W., Andrea-Nicole, Mark T.D.

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