Cosimo Toma

943 total citations
17 papers, 533 citations indexed

About

Cosimo Toma is a scholar working on Computational Theory and Mathematics, Molecular Biology and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Cosimo Toma has authored 17 papers receiving a total of 533 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Theory and Mathematics, 6 papers in Molecular Biology and 5 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Cosimo Toma's work include Computational Drug Discovery Methods (9 papers), Metabolomics and Mass Spectrometry Studies (4 papers) and Environmental Toxicology and Ecotoxicology (3 papers). Cosimo Toma is often cited by papers focused on Computational Drug Discovery Methods (9 papers), Metabolomics and Mass Spectrometry Studies (4 papers) and Environmental Toxicology and Ecotoxicology (3 papers). Cosimo Toma collaborates with scholars based in Italy, Netherlands and Germany. Cosimo Toma's co-authors include Emilio Benfenati, Domenico Gadaleta, Alessandra Roncaglioni, Anna Lombardo, Giovanna J. Lavado, Kamel Mansouri, Nicole Kleinstreuer, Agnes L. Karmaus, Diego Baderna and Claudia Cappelli and has published in prestigious journals such as The Science of The Total Environment, Environment International and Molecules.

In The Last Decade

Cosimo Toma

17 papers receiving 521 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Cosimo Toma Italy	12	257	128	111	84	59	17	533
Arianna Bassan Italy	13	245 1.0×	96 0.8×	135 1.2×	29 0.3×	82 1.4×	30	501
Shuaizhang Li United States	14	85 0.3×	190 1.5×	79 0.7×	76 0.9×	10 0.2×	24	543
Amber K. Goetz United States	14	84 0.3×	289 2.3×	279 2.5×	52 0.6×	113 1.9×	16	989
Catherine Mahony United Kingdom	17	156 0.6×	289 2.3×	255 2.3×	19 0.2×	89 1.5×	36	922
Terezinha Souza Brazil	15	39 0.2×	215 1.7×	105 0.9×	94 1.1×	86 1.5×	42	639
Agnes L. Karmaus United States	16	275 1.1×	209 1.6×	258 2.3×	15 0.2×	65 1.1×	28	790
Paolo Mazzatorta Italy	12	282 1.1×	114 0.9×	114 1.0×	26 0.3×	75 1.3×	24	483
Huazhang Huang United States	14	33 0.1×	240 1.9×	192 1.7×	109 1.3×	33 0.6×	24	700
Jason Yarbrough United States	12	84 0.3×	117 0.9×	104 0.9×	15 0.2×	26 0.4×	21	562
Patricia A. Escobar United States	13	42 0.2×	232 1.8×	266 2.4×	15 0.2×	75 1.3×	21	826

Countries citing papers authored by Cosimo Toma

Since Specialization

Citations

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

Fields of papers citing papers by Cosimo Toma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cosimo Toma. 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 Cosimo Toma. The network helps show where Cosimo Toma may publish in the future.

Co-authorship network of co-authors of Cosimo Toma

This figure shows the co-authorship network connecting the top 25 collaborators of Cosimo Toma. A scholar is included among the top collaborators of Cosimo Toma 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 Cosimo Toma. Cosimo Toma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Baderna, Diego, et al.. (2022). Skin sensitization quantitative QSAR models based on mechanistic structural alerts. Toxicology. 468. 153111–153111. 6 indexed citations

Toma, Cosimo, Claudia Cappelli, Alberto Manganaro, et al.. (2021). New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments. Molecules. 26(22). 6983–6983. 24 indexed citations

Carnesecchi, Edoardo, Cosimo Toma, Alessandra Roncaglioni, et al.. (2020). Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. The Science of The Total Environment. 735. 139243–139243. 32 indexed citations

Toma, Cosimo, et al.. (2020). Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis. Molecules. 25(3). 739–739. 8 indexed citations

Toma, Cosimo, Alberto Manganaro, Giuseppa Raitano, et al.. (2020). QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors. Molecules. 26(1). 127–127. 15 indexed citations

Lavado, Giovanna J., Domenico Gadaleta, Cosimo Toma, et al.. (2020). Zebrafish AC modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo. Ecotoxicology and Environmental Safety. 202. 110936–110936. 23 indexed citations

Cappelli, Claudia, Serena Manganelli, Cosimo Toma, Emilio Benfenati, & Enrico Mombelli. (2020). Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach. Molecular Informatics. 40(3). e2000072–e2000072. 3 indexed citations

Marzo, Marco, Giovanna J. Lavado, Alla P. Toropova, et al.. (2020). QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity. SAR and QSAR in environmental research. 31(3). 227–243. 20 indexed citations

Carnesecchi, Edoardo, Claus Svendsen, Nadia Quignot, et al.. (2019). Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment. Environment International. 133(Pt B). 105256–105256. 70 indexed citations

10.

Delp, Johannes, Susanne Hougaard Bennekou, Giada Carta, et al.. (2019). Development of a neurotoxicity assay that is tuned to detect mitochondrial toxicants. Archives of Toxicology. 93(6). 1585–1608. 35 indexed citations

11.

Gadaleta, Domenico, Cosimo Toma, Giovanna J. Lavado, et al.. (2019). SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. Journal of Cheminformatics. 11(1). 58–58. 123 indexed citations

12.

Khan, Kabiruddin, Diego Baderna, Claudia Cappelli, et al.. (2019). Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis. Aquatic Toxicology. 212. 162–174. 42 indexed citations

13.

Toma, Cosimo, Domenico Gadaleta, Alessandra Roncaglioni, et al.. (2018). QSAR Development for Plasma Protein Binding: Influence of the Ionization State. Pharmaceutical Research. 36(2). 28–28. 14 indexed citations

14.

Marzo, Marco, et al.. (2018). Impact of REACH legislation on the production and importation of CMR (carcinogen, mutagen and reproductive) and explosive chemicals in Italy from 2011 to 2015. Regulatory Toxicology and Pharmacology. 101. 166–171. 1 indexed citations

15.

Gadaleta, Domenico, Serena Manganelli, Alessandra Roncaglioni, et al.. (2018). QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis. Journal of Chemical Information and Modeling. 58(8). 1501–1517. 58 indexed citations

16.

Gadaleta, Domenico, Anna Lombardo, Cosimo Toma, & Emilio Benfenati. (2018). A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications. Journal of Cheminformatics. 10(1). 60–60. 57 indexed citations

17.

Zamfirache, Maria-Magdalena, et al.. (2010). Estimation of genotoxic potential of carbendazim in fenugreek. 20(2). 39–44. 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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