Omar Demerdash

2.0k total citations
30 papers, 1.0k citations indexed

About

Omar Demerdash is a scholar working on Molecular Biology, Materials Chemistry and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Omar Demerdash has authored 30 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 12 papers in Materials Chemistry and 9 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Omar Demerdash's work include Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (7 papers) and Advanced Chemical Physics Studies (7 papers). Omar Demerdash is often cited by papers focused on Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (7 papers) and Advanced Chemical Physics Studies (7 papers). Omar Demerdash collaborates with scholars based in United States, Russia and India. Omar Demerdash's co-authors include Teresa Head‐Gordon, Julie C. Mitchell, Yuezhi Mao, Martin Head‐Gordon, Jeremy C. Smith, Ronald E. Kalil, Mood Mohan, Michelle K. Kidder, Akshaya Kumar Das and Michael D. Daily and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and PLoS ONE.

In The Last Decade

Omar Demerdash

29 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Omar Demerdash United States 19 440 296 256 162 116 30 1.0k
Zhifeng Jing United States 15 630 1.4× 361 1.2× 590 2.3× 89 0.5× 61 0.5× 26 1.5k
Joshua A. Rackers United States 11 353 0.8× 366 1.2× 269 1.1× 82 0.5× 35 0.3× 16 885
Jiajing Zhang China 7 455 1.0× 337 1.1× 210 0.8× 86 0.5× 14 0.1× 16 819
Lea Thøgersen Denmark 18 516 1.2× 357 1.2× 136 0.5× 45 0.3× 23 0.2× 23 1.0k
Luca Larini United States 13 560 1.3× 115 0.4× 495 1.9× 81 0.5× 23 0.2× 20 1.2k
Sebastian Thallmair Germany 20 691 1.6× 358 1.2× 246 1.0× 63 0.4× 12 0.1× 47 1.2k
Miguel A. Soler Italy 20 465 1.1× 202 0.7× 115 0.4× 43 0.3× 16 0.1× 49 773
Yun Luo United States 21 1.5k 3.4× 383 1.3× 235 0.9× 112 0.7× 16 0.1× 84 2.3k
Silvina Matysiak United States 19 756 1.7× 163 0.6× 502 2.0× 24 0.1× 35 0.3× 54 1.2k
Esko Oksanen Sweden 19 602 1.4× 70 0.2× 291 1.1× 46 0.3× 29 0.3× 42 1.1k

Countries citing papers authored by Omar Demerdash

Since Specialization
Citations

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

Fields of papers citing papers by Omar Demerdash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Omar Demerdash

This figure shows the co-authorship network connecting the top 25 collaborators of Omar Demerdash. A scholar is included among the top collaborators of Omar Demerdash 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 Omar Demerdash. Omar Demerdash 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.
Prates, Érica T., Omar Demerdash, Manesh Shah, et al.. (2025). Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning. Computational and Structural Biotechnology Journal. 27. 2782–2795. 2 indexed citations
2.
Demerdash, Omar, et al.. (2024). TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets. Frontiers in Immunology. 15. 1426173–1426173. 9 indexed citations
3.
Mohan, Mood, et al.. (2024). Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents. Journal of Chemical Theory and Computation. 20(9). 3911–3926. 35 indexed citations
4.
Mohan, Mood, Omar Demerdash, Blake A. Simmons, et al.. (2024). Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents. ACS Omega. 9(17). 19548–19559. 20 indexed citations
5.
Smith, Micholas Dean, L. Darryl Quarles, Omar Demerdash, & Jeremy C. Smith. (2024). Drugging the entire human proteome: Are we there yet?. Drug Discovery Today. 29(3). 103891–103891.
6.
Mohan, Mood, Micholas Dean Smith, Omar Demerdash, Michelle K. Kidder, & Jeremy C. Smith. (2023). Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning. The Journal of Chemical Physics. 158(21). 18 indexed citations
7.
Mohan, Mood, Micholas Dean Smith, Omar Demerdash, et al.. (2023). Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids. ACS Sustainable Chemistry & Engineering. 11(20). 7809–7821. 25 indexed citations
8.
Mohan, Mood, Omar Demerdash, Blake A. Simmons, et al.. (2023). Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network. Green Chemistry. 25(9). 3475–3492. 51 indexed citations
9.
Cope, Kevin R., Érica T. Prates, John I. Miller, et al.. (2022). Exploring the role of plant lysin motif receptor-like kinases in regulating plant-microbe interactions in the bioenergy crop Populus. Computational and Structural Biotechnology Journal. 21. 1122–1139. 9 indexed citations
10.
Prates, Érica T., Michael R. Garvin, Irimpan I. Mathews, et al.. (2022). Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro. Nature Communications. 13(1). 5285–5285. 24 indexed citations
11.
Demerdash, Omar. (2021). Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction. Journal of Computer-Aided Molecular Design. 35(11). 1095–1123. 5 indexed citations
12.
Spooner, Catherine, et al.. (2020). Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets. Biochimica et Biophysica Acta (BBA) - General Subjects. 1864(4). 129535–129535. 13 indexed citations
13.
Tonelli, Marco, Woonghee Lee, Ziqing Lin, et al.. (2019). Solution structure of human myeloid-derived growth factor suggests a conserved function in the endoplasmic reticulum. Nature Communications. 10(1). 5612–5612. 14 indexed citations
14.
Demerdash, Omar, Utsab R. Shrestha, Loukas Petridis, et al.. (2019). Using Small-Angle Scattering Data and Parametric Machine Learning to Optimize Force Field Parameters for Intrinsically Disordered Proteins. Frontiers in Molecular Biosciences. 6. 64–64. 18 indexed citations
15.
Das, Akshaya Kumar, Omar Demerdash, & Teresa Head‐Gordon. (2018). Improvements to the AMOEBA Force Field by Introducing Anisotropic Atomic Polarizability of the Water Molecule. Journal of Chemical Theory and Computation. 14(12). 6722–6733. 35 indexed citations
16.
Cimermančič, Peter, Patrick Weinkam, T.J. Rettenmaier, et al.. (2016). CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites. Journal of Molecular Biology. 428(4). 709–719. 167 indexed citations
17.
Demerdash, Omar, et al.. (2013). Advanced Potential Energy Surfaces for Condensed Phase Simulation. Annual Review of Physical Chemistry. 65(1). 149–174. 57 indexed citations
18.
Demerdash, Omar & Julie C. Mitchell. (2012). Density‐cluster NMA: A new protein decomposition technique for coarse‐grained normal mode analysis. Proteins Structure Function and Bioinformatics. 80(7). 1766–1779. 14 indexed citations
19.
Demerdash, Omar, et al.. (2010). ReplicOpter: A replicate optimizer for flexible docking. Proteins Structure Function and Bioinformatics. 78(15). 3156–3165. 6 indexed citations
20.
Demerdash, Omar, Michael D. Daily, & Julie C. Mitchell. (2009). Structure-Based Predictive Models for Allosteric Hot Spots. PLoS Computational Biology. 5(10). e1000531–e1000531. 57 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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