Computational Toxicology

306 papers and 3.1k indexed citations i.

About

The 306 papers published in Computational Toxicology in the last decades have received a total of 3.1k indexed citations. Papers published in Computational Toxicology usually cover Computational Theory and Mathematics (155 papers), Molecular Biology (85 papers) and Health, Toxicology and Mutagenesis (75 papers) specifically the topics of Computational Drug Discovery Methods (155 papers), Animal testing and alternatives (60 papers) and Effects and risks of endocrine disrupting chemicals (53 papers). The most active scholars publishing in Computational Toxicology are Grace Patlewicz, Andrew Worth, Mark T.D. Cronin, Imran Shah, T.W. Schultz, Prachi Pradeep, Ann M. Richard, Kevin P. Cross, Judith C. Madden and David J. Ponting.

In The Last Decade

Fields of papers published in Computational Toxicology

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Computational Toxicology. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Computational Toxicology.

Countries where authors publish in Computational Toxicology

Since Specialization
Citations

This map shows the geographic impact of research published in Computational Toxicology. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Computational Toxicology with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computational Toxicology more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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2025