Todd M. Martin

5.1k total citations
43 papers, 2.1k citations indexed

About

Todd M. Martin is a scholar working on Computational Theory and Mathematics, Health, Toxicology and Mutagenesis and Environmental Chemistry. According to data from OpenAlex, Todd M. Martin has authored 43 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computational Theory and Mathematics, 11 papers in Health, Toxicology and Mutagenesis and 11 papers in Environmental Chemistry. Recurrent topics in Todd M. Martin's work include Computational Drug Discovery Methods (18 papers), Chemistry and Chemical Engineering (8 papers) and Analytical Chemistry and Chromatography (7 papers). Todd M. Martin is often cited by papers focused on Computational Drug Discovery Methods (18 papers), Chemistry and Chemical Engineering (8 papers) and Analytical Chemistry and Chromatography (7 papers). Todd M. Martin collaborates with scholars based in United States, Italy and Ghana. Todd M. Martin's co-authors include Douglas M. Young, Paul Harten, Alexander Tropsha, Hao Zhu, Douglas Young, Mace G. Barron, Raghuraman Venkatapathy, Ira Herskowitz, Marlis Dahl and Flora Banuett and has published in prestigious journals such as Cell, Chemosphere and Polymer.

In The Last Decade

Todd M. Martin

42 papers receiving 2.0k citations

Peers

Todd M. Martin

CTM

HTM

Cecilia Bossa Italy

Steven J. Enoch United Kingdom

Warren Casey United States

Alessandra Roncaglioni Italy

Judith C. Madden United Kingdom

David Allen United States

Manuela Pavan Italy

Robert D. Combes United Kingdom

Alexander Sedykh United States

Chihae Yang United States

Cecilia Bossa Italy

Todd M. Martin

864 ×1.1

CTM

680 ×1.0

439 ×1.0

HTM

175 ×0.6

210 ×0.9

Citations per year, relative to Todd M. Martin Todd M. Martin (= 1×) peers Cecilia Bossa

Countries citing papers authored by Todd M. Martin

Since Specialization

Citations

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

Fields of papers citing papers by Todd M. Martin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Todd M. Martin. 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 Todd M. Martin. The network helps show where Todd M. Martin may publish in the future.

Co-authorship network of co-authors of Todd M. Martin

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

All Works

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20 of 20 papers shown

Harten, Paul, Todd M. Martin, Daniel T. Chang, & Douglas Young. (2022). Finding Potential Replacements for TRI Solvents Using the Environmental Impact of the Average Solvent. Journal of Solution Chemistry. 51(7). 838–849. 1 indexed citations

Dilger, Jonathan M., et al.. (2021). Detection and toxicity modeling of anthraquinone dyes and chlorinated side products from a colored smoke pyrotechnic reaction. Chemosphere. 287(Pt 1). 131845–131845. 9 indexed citations

Patlewicz, Grace, Jeffry L. Dean, Catherine F. Gibbons, et al.. (2021). Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity. Computational Toxicology. 20. 100185–100185. 5 indexed citations

Pradeep, Prachi, Richard Judson, David M. DeMarini, et al.. (2021). An evaluation of existing QSAR models and structural alerts and development of new ensemble models for genotoxicity using a newly compiled experimental dataset. Computational Toxicology. 18. 100167–100167. 16 indexed citations

Martin, Todd M., et al.. (2020). An automated framework for compiling and integrating chemical hazard data. Clean Technologies and Environmental Policy. 22(2). 441–458. 27 indexed citations

Rao, T. Sudhakar, et al.. (2019). Linking Molecular Structure via Functional Group to Chemical Literature for Establishing a Reaction Lineage for Application to Alternatives Assessment. ACS Sustainable Chemistry & Engineering. 7(8). 7630–7641. 3 indexed citations

Martin, Todd M.. (2016). A framework for an alternatives assessment dashboard for evaluating chemical alternatives applied to flame retardants for electronic applications. Clean Technologies and Environmental Policy. 19(4). 1067–1086. 5 indexed citations

Martin, Todd M.. (2016). Prediction ofin vitroandin vivooestrogen receptor activity using hierarchical clustering. SAR and QSAR in environmental research. 27(1). 17–30. 11 indexed citations

Carriger, John F., Todd M. Martin, & Mace G. Barron. (2016). A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors. Aquatic Toxicology. 180. 11–24. 18 indexed citations

10.

Barron, Mace G., et al.. (2015). MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development. Aquatic Toxicology. 161. 102–107. 91 indexed citations

11.

Raitano, Giuseppa, et al.. (2013). Comparison of In Silico Models for Prediction of Mutagenicity. Journal of Environmental Science and Health Part C. 31(1). 45–66. 87 indexed citations

12.

Baten, Jasper M. van, et al.. (2011). Implementation of the waste reduction (WAR) algorithm utilizing flowsheet monitoring. Computers & Chemical Engineering. 35(12). 2680–2686. 23 indexed citations

13.

Cassano, Antonio, Alberto Manganaro, Todd M. Martin, et al.. (2010). CAESAR models for developmental toxicity. Chemistry Central Journal. 4(S1). S4–S4. 96 indexed citations

14.

Benfenati, Emilio, Romualdo Benigni, David M. DeMarini, et al.. (2009). Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives. Journal of Environmental Science and Health Part C. 27(2). 57–90. 90 indexed citations

15.

Moudgal, Chandrika, et al.. (2008). Application of QSARs and VFARs to the rapid risk assessment process at US EPA. SAR and QSAR in environmental research. 19(5-6). 579–587. 3 indexed citations

16.

Beutler, Ivan F., et al.. (2004). Financial Prudence and Next Generation Financial Strain. SSRN Electronic Journal. 47 indexed citations

17.

Martin, Todd M., et al.. (2002). Preparation of budesonide and budesonide-PLA microparticles using supercritical fluid precipitation technology. AAPS PharmSciTech. 3(3). 16–26. 41 indexed citations

18.

Martin, Todd M., et al.. (2001). Demixing Pressure Measurements of Aerosol-OT in Ethane + Alcohol Solvent Mixtures. Journal of Chemical & Engineering Data. 46(3). 769–772. 5 indexed citations

19.

Martin, Todd M., et al.. (1999). Measurements and modeling of cloud point behavior for polypropylene/n-pentane and polypropylene/n-pentane/carbon dioxide mixtures at high pressure. Fluid Phase Equilibria. 154(2). 241–259. 23 indexed citations

20.

Schulz, Burkhard, Flora Banuett, Marlis Dahl, et al.. (1990). The b alleles of U. maydis, whose combinations program pathogenic development, code for polypeptides containing a homeodomain-related motif. Cell. 60(2). 295–306. 333 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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