Matthew J. O’Meara
- Molecular Biology top 5%
- Computational Theory and Mathematics top 0.5%
- Materials Chemistry top 10%
- Radiology, Nuclear Medicine and Imaging top 5%
- Infectious Diseases top 10%
- Co-authors
- Tanja KortemmeBrian KuhlmanAndrew Leaver‐FayDavid BakerFrank DiMaioPhilip BradleyJeffrey J. GrayBrian K. Shoichet
- Topics
- Computational Drug Discovery Methods (6 papers)Protein Structure and Dynamics (5 papers)RNA and protein synthesis mechanisms (4 papers)
- Journals
- NatureNature CommunicationsPLoS ONE
- Partner nations
- United StatesCanadaUkraine
In The Last Decade
Matthew J. O’Meara
28 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Molecular Biology 2.0k
- Computational Theory and Mathematics 656
- Materials Chemistry 547
- Radiology, Nuclear Medicine and Imaging 257
- Infectious Diseases 240
Countries citing papers authored by Matthew J. O’Meara
This map shows the geographic impact of Matthew J. O’Meara'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 J. O’Meara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew J. O’Meara more than expected).
Fields of papers citing papers by Matthew J. O’Meara
This network shows the impact of papers produced by Matthew J. O’Meara. 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 J. O’Meara. The network helps show where Matthew J. O’Meara may publish in the future.
Co-authorship network of co-authors of Matthew J. O’Meara
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew J. O’Meara. A scholar is included among the top collaborators of Matthew J. O’Meara 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 J. O’Meara. Matthew J. O’Meara is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 30 | |
| 4 | A human liver organoid screening platform for DILI risk predictionbreakdown → | 108 |
| 5 | 12 | |
| 6 | 22 | |
| 7 | 3 | |
| 8 | 135 | |
| 9 | 9 | |
| 10 | A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. | 1 |
| 11 | 36 | |
| 12 | Ultra-large library docking for discovering new chemotypesbreakdown → | 619 |
| 13 | 43 | |
| 14 | 53 | |
| 15 | 30 | |
| 16 | The Rosetta All-Atom Energy Function for Macromolecular Modeling and Designbreakdown → | 942 |
| 17 | 12 | |
| 18 | 49 | |
| 19 | 70 | |
| 20 | 163 |
About Matthew J. O’Meara
Matthew J. O’Meara is a scholar working on Computational Theory and Mathematics, Molecular Biology and Infectious Diseases, having authored 29 papers that have together received 2.7k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Protein Structure and Dynamics (5 papers) and RNA and protein synthesis mechanisms (4 papers). The work is most often cited by research in Computational Theory and Mathematics (656 citations), Molecular Biology (2.0k citations) and Infectious Diseases (240 citations). Matthew J. O’Meara has collaborated with scholars based in United States, Canada and Ukraine. Frequent co-authors include Tanja Kortemme, Brian Kuhlman, Andrew Leaver‐Fay, David Baker, Frank DiMaio, Philip Bradley, Jeffrey J. Gray, Brian K. Shoichet, Jiankun Lyu and Yurii S. Moroz. Their work appears in journals such as Nature, Nature Communications and PLoS ONE.
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.