Thomas M. Whitehead

1.1k total citations
19 papers, 518 citations indexed

About

Thomas M. Whitehead is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Thomas M. Whitehead has authored 19 papers receiving a total of 518 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 5 papers in Molecular Biology and 5 papers in Materials Chemistry. Recurrent topics in Thomas M. Whitehead's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). Thomas M. Whitehead is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). Thomas M. Whitehead collaborates with scholars based in United Kingdom, United States and South Sudan. Thomas M. Whitehead's co-authors include Eric K. Chu, Arthur S. Slutsky, G. J. Conduit, Matthew Segall, Adams Amantana, Patrick L. Iversen, Benedict Irwin, Hong M. Moulton, Peter Hunt and Julian Levell and has published in prestigious journals such as SHILAP Revista de lepidopterología, Critical Care Medicine and Bioconjugate Chemistry.

In The Last Decade

Thomas M. Whitehead

18 papers receiving 500 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas M. Whitehead United Kingdom 9 214 123 114 73 47 19 518
Yanzhu Chen China 15 104 0.5× 17 0.1× 69 0.6× 40 0.5× 8 0.2× 45 680
Feihong Wu China 10 238 1.1× 49 0.4× 42 0.4× 38 0.5× 3 0.1× 28 414
Manuel Escobar Spain 12 96 0.4× 101 0.8× 88 0.8× 21 0.3× 3 0.1× 30 496
Kenji Horiuchi Japan 16 64 0.3× 33 0.3× 13 0.1× 209 2.9× 6 0.1× 57 690
Mujeeb A. Sultan Saudi Arabia 8 58 0.3× 9 0.1× 35 0.3× 32 0.4× 6 0.1× 27 469
Shining Wang United States 13 116 0.5× 15 0.1× 24 0.2× 54 0.7× 44 441
SD Kim South Korea 10 55 0.3× 18 0.1× 9 0.1× 101 1.4× 4 0.1× 16 324
Shinichi Yasuda Japan 11 66 0.3× 21 0.2× 17 0.1× 162 2.2× 6 0.1× 34 459
R. A. George India 10 83 0.4× 5 0.0× 59 0.5× 10 0.1× 52 1.1× 24 293

Countries citing papers authored by Thomas M. Whitehead

Since Specialization
Citations

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

Fields of papers citing papers by Thomas M. Whitehead

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas M. Whitehead

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

All Works

19 of 19 papers shown
1.
Whitehead, Thomas M., et al.. (2023). Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling. Journal of Chemical Information and Modeling. 64(7). 2624–2636. 4 indexed citations
2.
Obrezanova, Olga, Thomas M. Whitehead, Andreas Bender, et al.. (2022). Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure. Molecular Pharmaceutics. 19(5). 1488–1504. 46 indexed citations
3.
Drakopoulos, Stavros X., Azarmidokht Gholamipour‐Shirazi, Paul M. Macdonald, et al.. (2021). Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning. Cell Reports Physical Science. 2(12). 100683–100683. 36 indexed citations
4.
Irwin, Benedict, Dmitriy S. Chekmarev, Shyam Vyas, et al.. (2021). Imputation of sensory properties using deep learning. Journal of Computer-Aided Molecular Design. 35(11). 1125–1140. 6 indexed citations
5.
Whitehead, Thomas M., et al.. (2021). Accelerating the Design of Automotive Catalyst Products Using Machine Learning. Johnson Matthey Technology Review. 66(2). 130–136. 2 indexed citations
6.
Whitehead, Thomas M.. (2021). Hyperparameter-free Regularization by Sampling from an Infinite Space of Neural Networks. International Journal of Artificial Intelligence Tools. 30(2). 2150008–2150008.
7.
Irwin, Benedict, et al.. (2021). Deep imputation on large‐scale drug discovery data. SHILAP Revista de lepidopterología. 2(3). 6 indexed citations
8.
Irwin, Benedict, Julian Levell, Thomas M. Whitehead, Matthew Segall, & G. J. Conduit. (2020). Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data. Journal of Chemical Information and Modeling. 60(6). 2848–2857. 39 indexed citations
9.
Irwin, Benedict, et al.. (2020). Imputation Versus Prediction: Applications in Machine Learning for Drug Discovery. SHILAP Revista de lepidopterología. 2(2). 12 indexed citations
10.
Whitehead, Thomas M., Benedict Irwin, Peter Hunt, Matthew Segall, & G. J. Conduit. (2019). Imputation of Assay Bioactivity Data Using Deep Learning. Journal of Chemical Information and Modeling. 59(3). 1197–1204. 47 indexed citations
11.
Webster, Helen, Thomas M. Whitehead, & David J. Thomson. (2018). Parameterizing Unresolved Mesoscale Motions in Atmospheric Dispersion Models. Journal of Applied Meteorology and Climatology. 57(3). 645–657. 11 indexed citations
12.
Whitehead, Thomas M. & G. J. Conduit. (2018). Multiparticle instability in a spin-imbalanced Fermi gas. Physical review. B.. 97(1). 2 indexed citations
13.
Whitehead, Thomas M., et al.. (2016). Pseudopotential for the two-dimensional contact interaction. Physical review. A. 93(4). 12 indexed citations
14.
Whitehead, Thomas M. & G. J. Conduit. (2016). Pseudopotentials for an ultracold dipolar gas. Physical review. A. 93(2). 4 indexed citations
15.
Whitehead, Thomas M., et al.. (2016). Jastrow correlation factor for periodic systems. Physical review. B.. 94(3). 7 indexed citations
16.
Amantana, Adams, et al.. (2007). Pharmacokinetics, Biodistribution, Stability and Toxicity of a Cell-Penetrating Peptide−Morpholino Oligomer Conjugate. Bioconjugate Chemistry. 18(4). 1325–1331. 165 indexed citations
17.
Chu, Eric K., Thomas M. Whitehead, & Arthur S. Slutsky. (2004). Effects of cyclic opening and closing at low- and high-volume ventilation on bronchoalveolar lavage cytokines*. Critical Care Medicine. 32(1). 168–174. 116 indexed citations
18.
Whitehead, Thomas M., et al.. (2001). Digitizing Collections. Collection Management. 26(3). 3–13. 1 indexed citations
19.
Hale, Ann, et al.. (1993). Serum urate as a free radical scavenger in diabetics. Journal of Medical Systems. 17(3-4). 233–237. 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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