Prachi Pradeep

805 total citations
23 papers, 442 citations indexed

About

Prachi Pradeep is a scholar working on Computational Theory and Mathematics, Health, Toxicology and Mutagenesis and Small Animals. According to data from OpenAlex, Prachi Pradeep has authored 23 papers receiving a total of 442 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Theory and Mathematics, 13 papers in Health, Toxicology and Mutagenesis and 5 papers in Small Animals. Recurrent topics in Prachi Pradeep's work include Computational Drug Discovery Methods (16 papers), Effects and risks of endocrine disrupting chemicals (10 papers) and Animal testing and alternatives (5 papers). Prachi Pradeep is often cited by papers focused on Computational Drug Discovery Methods (16 papers), Effects and risks of endocrine disrupting chemicals (10 papers) and Animal testing and alternatives (5 papers). Prachi Pradeep collaborates with scholars based in United States, Germany and Japan. Prachi Pradeep's co-authors include Grace Patlewicz, Richard Judson, Imran Shah, Katie Paul Friedman, Stephen J. Merrill, Richard J. Povinelli, Shannon M. White, John F. Wambaugh, Ly Pham and Russell S. Thomas and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Regulatory Toxicology and Pharmacology.

In The Last Decade

Prachi Pradeep

23 papers receiving 429 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Prachi Pradeep United States	14	192	166	127	68	64	23	442
Claire M. Ellison United Kingdom	11	158 0.8×	242 1.5×	77 0.6×	55 0.8×	34 0.5×	14	469
Worth Andrew	15	183 1.0×	152 0.9×	94 0.7×	44 0.6×	84 1.3×	36	449
Arianna Bassan Italy	13	135 0.7×	245 1.5×	71 0.6×	57 0.8×	71 1.1×	30	501
Chanita Kuseva Bulgaria	14	151 0.8×	262 1.6×	164 1.3×	88 1.3×	48 0.8×	20	582
Nicholas Ball United States	15	209 1.1×	233 1.4×	256 2.0×	87 1.3×	78 1.2×	34	708
Atanas Chapkanov Bulgaria	8	99 0.5×	172 1.0×	70 0.6×	55 0.8×	26 0.4×	13	328
Yuki Sakuratani Japan	10	145 0.8×	134 0.8×	68 0.5×	36 0.5×	34 0.5×	23	360
Robert Diderich France	4	102 0.5×	150 0.9×	59 0.5×	55 0.8×	28 0.4×	5	309
Elisabet Berggren Italy	10	318 1.7×	116 0.7×	199 1.6×	93 1.4×	80 1.3×	23	688
Anna Lowit United States	14	221 1.2×	102 0.6×	225 1.8×	46 0.7×	61 1.0×	25	689

Countries citing papers authored by Prachi Pradeep

Since Specialization

Citations

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

Fields of papers citing papers by Prachi Pradeep

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prachi Pradeep

This figure shows the co-authorship network connecting the top 25 collaborators of Prachi Pradeep. A scholar is included among the top collaborators of Prachi Pradeep 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 Prachi Pradeep. Prachi Pradeep 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

Singh, Ajay Vikram, Peter Laux, Prachi Pradeep, et al.. (2024). AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability. SHILAP Revista de lepidopterología. 6. 1461587–1461587. 33 indexed citations

Dawson, Daniel E., Christopher Lau, Prachi Pradeep, et al.. (2023). A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. Toxics. 11(2). 98–98. 31 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

Pradeep, Prachi, Grace Patlewicz, Robert G. Pearce, et al.. (2020). Using chemical structure information to develop predictive models for in vitro toxicokinetic parameters to inform high-throughput risk-assessment. Computational Toxicology. 16. 100136–100136. 30 indexed citations

Fantke, Peter, Richard Judson, Xiaoqing Chang, et al.. (2020). Integrating endocrine-related health effects into comparative human toxicity characterization. The Science of The Total Environment. 762. 143874–143874. 14 indexed citations

Pradeep, Prachi, Katie Paul Friedman, & Richard Judson. (2020). Structure-based QSAR models to predict repeat dose toxicity points of departure. Computational Toxicology. 16(November 2020). 100139–100139. 27 indexed citations

Pham, Ly, Prachi Pradeep, Matthew T. Martin, et al.. (2020). Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels. Computational Toxicology. 15(August 2020). 100126–100126. 48 indexed citations

Nelms, Mark, Prachi Pradeep, & Grace Patlewicz. (2019). Evaluating potential refinements to existing Threshold of Toxicological Concern (TTC) values for environmentally-relevant compounds. Regulatory Toxicology and Pharmacology. 109. 104505–104505. 16 indexed citations

10.

Pham, Ly, Thomas Sheffield, Prachi Pradeep, et al.. (2019). Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation. Current Opinion in Toxicology. 15. 40–47. 16 indexed citations

11.

Pradeep, Prachi, Laura M. Carlson, Richard Judson, Geniece M. Lehmann, & Grace Patlewicz. (2018). Integrating data gap filling techniques: A case study predicting TEFs for neurotoxicity TEQs to facilitate the hazard assessment of polychlorinated biphenyls. Regulatory Toxicology and Pharmacology. 101. 12–23. 13 indexed citations

12.

Baxter, Lisa, Kathie L. Dionisio, Prachi Pradeep, Kristen M. Rappazzo, & Lucas Neas. (2018). Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality. Journal of Exposure Science & Environmental Epidemiology. 29(4). 557–567. 4 indexed citations

13.

Fitzpatrick, Jeremy, Prachi Pradeep, Agnes L. Karmaus, & Grace Patlewicz. (2018). Using Chemical and Biological Descriptors to Develop Predictive Models for Rat Acute Oral Toxicity. Figshare. 1 indexed citations

14.

Pradeep, Prachi, Kamel Mansouri, Grace Patlewicz, & Richard Judson. (2017). A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols. Computational Toxicology. 4. 22–30. 15 indexed citations

15.

Viñas, René, Amber Nagy, Prachi Pradeep, et al.. (2017). Deriving a provisional tolerable intake for intravenous exposure to silver nanoparticles released from medical devices. Regulatory Toxicology and Pharmacology. 85. 108–118. 11 indexed citations

16.

Patlewicz, Grace, et al.. (2017). Navigating through the minefield of read-across tools: A review of in silico tools for grouping. Computational Toxicology. 3. 1–18. 73 indexed citations

17.

Pradeep, Prachi, Richard J. Povinelli, Shannon M. White, & Stephen J. Merrill. (2016). An ensemble model of QSAR tools for regulatory risk assessment. Journal of Cheminformatics. 8(1). 48–48. 47 indexed citations

18.

Pradeep, Prachi, Richard J. Povinelli, Stephen J. Merrill, Serdar Bozdag, & Daniel S. Sem. (2015). Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction. Molecular Informatics. 34(4). 236–245. 9 indexed citations

19.

Brown, Ronald P., et al.. (2013). Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives. 3 indexed citations

20.

Pradeep, Prachi. (2011). Heuristics for scaling up distributed protein docking. e-publications - Marquette (Marquette University). 1 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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