Matthew Holcomb

437 total citations
21 papers, 263 citations indexed

About

Matthew Holcomb is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Matthew Holcomb has authored 21 papers receiving a total of 263 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 6 papers in Computational Theory and Mathematics and 4 papers in Organic Chemistry. Recurrent topics in Matthew Holcomb's work include Chemical Synthesis and Analysis (6 papers), Computational Drug Discovery Methods (6 papers) and Protein Structure and Dynamics (5 papers). Matthew Holcomb is often cited by papers focused on Chemical Synthesis and Analysis (6 papers), Computational Drug Discovery Methods (6 papers) and Protein Structure and Dynamics (5 papers). Matthew Holcomb collaborates with scholars based in United States, Australia and Switzerland. Matthew Holcomb's co-authors include Stefano Forli, Ya‐Ting Chang, David S. Goodsell, Floyd E. Romesberg, Vivian T. Dien, Aaron W. Feldman, Emil C. Fischer, Tammy J. Dwyer, Ramkrishna Adhikary and Jörg Zimmermann and has published in prestigious journals such as Science, Journal of the American Chemical Society and SHILAP Revista de lepidopterología.

In The Last Decade

Matthew Holcomb

17 papers receiving 258 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Holcomb United States 10 176 57 36 27 25 21 263
Mykhailo Girych Finland 10 208 1.2× 33 0.6× 22 0.6× 17 0.6× 35 1.4× 17 303
Andrei Ursu United States 13 233 1.3× 36 0.6× 36 1.0× 15 0.6× 17 0.7× 18 332
Eduard Puig Spain 8 232 1.3× 24 0.4× 48 1.3× 70 2.6× 28 1.1× 11 344
Alison M. Maxwell United States 5 172 1.0× 12 0.2× 22 0.6× 46 1.7× 13 0.5× 5 251
Lianghe Mei China 8 221 1.3× 41 0.7× 29 0.8× 19 0.7× 60 2.4× 13 288
Minkoo Ahn United Kingdom 8 234 1.3× 16 0.3× 44 1.2× 59 2.2× 20 0.8× 12 367
Federica Scollo Czechia 7 265 1.5× 18 0.3× 31 0.9× 26 1.0× 17 0.7× 13 396
Sonia Ciudad Spain 8 243 1.4× 24 0.4× 42 1.2× 26 1.0× 24 1.0× 9 354
Sara Bologna Italy 9 191 1.1× 15 0.3× 18 0.5× 73 2.7× 42 1.7× 11 292
Melissa Huang United Kingdom 4 231 1.3× 11 0.2× 46 1.3× 28 1.0× 35 1.4× 5 394

Countries citing papers authored by Matthew Holcomb

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Holcomb

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Holcomb

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Holcomb. A scholar is included among the top collaborators of Matthew Holcomb 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 Matthew Holcomb. Matthew Holcomb 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.
Fernández‐Quintero, Monica L., et al.. (2025). AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold. Journal of Chemical Information and Modeling. 66(1). 152–166. 1 indexed citations
2.
Holcomb, Matthew, et al.. (2025). A quantitative analysis of ligand binding at the protein-lipid bilayer interface. Communications Chemistry. 8(1). 89–89. 3 indexed citations
3.
Holcomb, Matthew, et al.. (2025). Structure-based rational design of covalent probes. Communications Chemistry. 8(1). 242–242.
4.
Woods, Emily C., Matthew Holcomb, John M. Bennett, et al.. (2025). An mRNA Display Approach for Covalent Targeting of a Staphylococcus aureus Virulence Factor. Journal of the American Chemical Society. 147(10). 8312–8325. 2 indexed citations
5.
6.
Santos‐Martins, Diogo, Jérôme Eberhardt, Matthew Holcomb, et al.. (2025). Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond. Journal of Chemical Information and Modeling. 65(24). 13045–13050.
7.
Boatner, Lisa M., Jérôme Eberhardt, Matthew Holcomb, et al.. (2025). CIAA: Integrated Proteomics and Structural Modeling for Understanding Cysteine Reactivity with Iodoacetamide Alkyne. ACS Chemical Biology. 20(7). 1669–1682.
8.
Eberhardt, Jérôme, Matthew Holcomb, Monica L. Fernández‐Quintero, et al.. (2024). CosolvKit: a Versatile Tool for Cosolvent MD Preparation and Analysis. Journal of Chemical Information and Modeling. 64(21). 8227–8235. 1 indexed citations
9.
Silvestri, Anthony P., Dillon T. Flood, Matthew Holcomb, et al.. (2023). Stretching Peptides to Generate Small Molecule β-Strand Mimics. ACS Central Science. 9(4). 648–656. 13 indexed citations
10.
Zucca, Stefano, Matthew Holcomb, Dipak N. Patil, et al.. (2023). Orphan receptor GPR158 serves as a metabotropic glycine receptor: mGlyR. Science. 379(6639). 1352–1358. 40 indexed citations
11.
Bianco, Giulia, et al.. (2023). Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species. Journal of Chemical Information and Modeling. 63(17). 5631–5640. 3 indexed citations
12.
Lopez‐Silva, Tania L., et al.. (2022). Direct observation of peptide hydrogel self-assembly. Chemical Science. 13(34). 10020–10028. 15 indexed citations
13.
Holcomb, Matthew, Diogo Santos‐Martins, Andreas F. Tillack, & Stefano Forli. (2022). Performance evaluation of flexible macrocycle docking in AutoDock. SHILAP Revista de lepidopterología. 3. e18–e18. 8 indexed citations
14.
Holcomb, Matthew, Ya‐Ting Chang, David S. Goodsell, & Stefano Forli. (2022). Evaluation of AlphaFold2 structures as docking targets. Protein Science. 32(1). e4530–e4530. 58 indexed citations
15.
Peters, David S., et al.. (2020). Initial Analysis of the Arylomycin D Antibiotics. Journal of Natural Products. 83(7). 2112–2121. 11 indexed citations
16.
Sun, Delin, William F. Bennett, Matthew Holcomb, et al.. (2020). Assessing the Perturbing Effects of Drugs on Lipid Bilayers Using Gramicidin Channel-Based In Silico and In Vitro Assays. Journal of Medicinal Chemistry. 63(20). 11809–11818. 9 indexed citations
17.
Derosa, Joseph, Miriam L. O’Duill, Matthew Holcomb, et al.. (2018). Copper-Catalyzed Chan–Lam Cyclopropylation of Phenols and Azaheterocycles. The Journal of Organic Chemistry. 83(7). 3417–3425. 29 indexed citations
18.
Holcomb, Matthew, Ramkrishna Adhikary, Jörg Zimmermann, & Floyd E. Romesberg. (2017). Topological Evidence of Previously Overlooked Ni+1–H···Ni H-Bonds and Their Contribution to Protein Structure and Stability. The Journal of Physical Chemistry A. 122(1). 446–450. 11 indexed citations
19.
Adhikary, Ramkrishna, Jian Liu, Jörg Zimmermann, et al.. (2017). Conformational Heterogeneity and DNA Recognition by the Morphogen Bicoid. Biochemistry. 56(22). 2787–2793. 6 indexed citations
20.
Holcomb, Matthew, Yonghong Ding, Daying Dai, et al.. (2015). RNA-Sequencing Analysis of Messenger RNA/MicroRNA in a Rabbit Aneurysm Model Identifies Pathways and Genes of Interest. American Journal of Neuroradiology. 36(9). 1710–1715. 18 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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