M Cronin

44.4k total citations · 5 hit papers
359 papers, 30.4k citations indexed

About

M Cronin is a scholar working on Computational Theory and Mathematics, Health, Toxicology and Mutagenesis and Small Animals. According to data from OpenAlex, M Cronin has authored 359 papers receiving a total of 30.4k indexed citations (citations by other indexed papers that have themselves been cited), including 200 papers in Computational Theory and Mathematics, 109 papers in Health, Toxicology and Mutagenesis and 70 papers in Small Animals. Recurrent topics in M Cronin's work include Computational Drug Discovery Methods (199 papers), Animal testing and alternatives (69 papers) and Environmental Toxicology and Ecotoxicology (61 papers). M Cronin is often cited by papers focused on Computational Drug Discovery Methods (199 papers), Animal testing and alternatives (69 papers) and Environmental Toxicology and Ecotoxicology (61 papers). M Cronin collaborates with scholars based in United Kingdom, United States and Italy. M Cronin's co-authors include Marián Valko, Milan Mazúr, Ján Moncóľ, Dieter Leibfritz, Joshua Telser, Harry Morris, T.W. Schultz, John C. Dearden, Judith C. Madden and Steven J. Enoch and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

M Cronin

351 papers receiving 29.1k citations

Hit Papers

Free radicals and antioxidants in normal physiological... 1994 2026 2004 2015 2006 2005 2013 2003 1994 2.5k 5.0k 7.5k 10.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M Cronin United Kingdom 62 8.4k 7.4k 5.6k 3.6k 2.9k 359 30.4k
Kamil Kuča Czechia 80 6.5k 0.8× 2.7k 0.4× 3.1k 0.5× 3.4k 0.9× 12.7k 4.3× 1.1k 31.6k
F. Peter Guengerich United States 127 27.2k 3.2× 4.8k 0.7× 4.2k 0.7× 4.5k 1.3× 3.9k 1.3× 941 68.3k
Ivonne M.C.M. Rietjens Netherlands 67 5.9k 0.7× 829 0.1× 2.3k 0.4× 1.7k 0.5× 2.5k 0.9× 565 17.8k
Raymond R. Tice United States 55 7.8k 0.9× 913 0.1× 7.2k 1.3× 980 0.3× 4.4k 1.5× 158 23.6k
Mohammad Abdollahı Iran 93 8.5k 1.0× 517 0.1× 4.2k 0.8× 1.5k 0.4× 8.9k 3.0× 1.1k 39.3k
Nico Vermeulen Netherlands 58 5.9k 0.7× 1.9k 0.3× 4.7k 0.8× 1.7k 0.5× 1.3k 0.5× 469 19.2k
Bruce D. Hammock United States 109 18.9k 2.3× 686 0.1× 3.3k 0.6× 5.1k 1.4× 3.9k 1.3× 1.4k 57.6k
Lawrence J. Marnett United States 96 13.3k 1.6× 796 0.1× 1.2k 0.2× 5.8k 1.6× 2.0k 0.7× 518 35.2k
Stephen Safe United States 107 16.8k 2.0× 1.1k 0.1× 21.9k 3.9× 2.7k 0.7× 1.7k 0.6× 822 51.9k
Peter J. O’Brien Canada 78 8.3k 1.0× 718 0.1× 1.2k 0.2× 2.2k 0.6× 1.6k 0.5× 403 22.4k

Countries citing papers authored by M Cronin

Since Specialization
Citations

This map shows the geographic impact of M Cronin's research. 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 M Cronin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M Cronin more than expected).

Fields of papers citing papers by M Cronin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M Cronin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M Cronin. The network helps show where M Cronin may publish in the future.

Co-authorship network of co-authors of M Cronin

This figure shows the co-authorship network connecting the top 25 collaborators of M Cronin. A scholar is included among the top collaborators of M Cronin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M Cronin. M Cronin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Firman, James W., et al.. (2025). Conservative consensus QSAR approach for the prediction of rat acute oral toxicity. Computational Toxicology. 35. 100374–100374.
2.
Cronin, M, Maria Teresa Baltazar, Tara S. Barton‐Maclaren, et al.. (2025). Report on the European Partnership for Alternative Approaches to Animal Testing (EPAA) “New Approach Methodologies (NAMs) User Forum Kick-Off Workshop”. Regulatory Toxicology and Pharmacology. 159. 105796–105796.
3.
Lawson, Thomas N., Claudia Rivetti, Carlos Barata, et al.. (2025). Substantiating chemical groups for read-across using molecular response profiles. Regulatory Toxicology and Pharmacology. 162. 105894–105894. 1 indexed citations
4.
Cronin, M, Hassan Basiri, Steven J. Enoch, et al.. (2024). The predictivity of QSARs for toxicity: Recommendations for improving model performance. Computational Toxicology. 33. 100338–100338. 10 indexed citations
6.
Botham, Philip A., M Cronin, A Currie, et al.. (2023). Analysis of health concerns not addressed by REACH for low tonnage chemicals and opportunities for new approach methodology. Archives of Toxicology. 97(12). 3075–3083. 6 indexed citations
7.
Cronin, M, Katharine Briggs, Steven J. Enoch, et al.. (2023). Making in silico predictive models for toxicology FAIR. Regulatory Toxicology and Pharmacology. 140. 105385–105385. 17 indexed citations
8.
Cronin, M, Nicholas Ball, Sonja Beken, et al.. (2023). Exposure considerations in human safety assessment: Report from an EPAA Partners’ Forum. Regulatory Toxicology and Pharmacology. 144. 105483–105483. 5 indexed citations
10.
Pawar, Gopal, et al.. (2019). In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Frontiers in Pharmacology. 10. 561–561. 47 indexed citations
11.
Gibson, Laura, et al.. (2018). Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert. Journal of Chemical Information and Modeling. 58(3). 673–682. 27 indexed citations
12.
Huang, Tao, et al.. (2017). Development of thresholds of excess toxicity for environmental species and their application to identification of modes of acute toxic action. The Science of The Total Environment. 616-617. 491–499. 30 indexed citations
13.
Cronin, M & Andrea-Nicole Richarz. (2017). Relationship Between Adverse Outcome Pathways and Chemistry-Based In Silico Models to Predict Toxicity. Liverpool John Moores University. 3(4). 286–297. 28 indexed citations
14.
Cronin, M. (2013). Chemical toxicity prediction : category formation and read-across. 24 indexed citations
15.
Przybylak, Katarzyna R. & M Cronin. (2011). In Silico Studies of the Relationship Between Chemical Structure and Drug Induced Phospholipidosis. Molecular Informatics. 30(5). 415–429. 20 indexed citations
16.
Cronin, M & Judith C. Madden. (2010). In silico toxicology : principles and applications. 82 indexed citations
17.
Dearden, John C., M Cronin, & Klaus L.E. Kaiser. (2009). How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR and QSAR in environmental research. 20(3-4). 241–266. 361 indexed citations
18.
Worth, Andrew & M Cronin. (2001). Prediction Models for Eye Irritation Potential Based on Endpoints of the HETCAM and Neutral Red Uptake Tests. PubMed. 14(3). 143–156. 9 indexed citations
19.
Cronin, M. (1998). Computer‐aided Prediction of Drug Toxicity in High Throughput Screening. Pharmacy and Pharmacology Communications. 4(3). 157–163. 11 indexed citations
20.
Dearden, John C., et al.. (1998). QSAR Study of the α1‐Adrenoceptor Antagonist Activity of WB4101 Derivatives. Pharmacy and Pharmacology Communications. 4(2). 89–93. 2 indexed citations

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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