Jeff Guo
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Computational Drug Discovery Methods 6
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- Machine Learning in Materials Science 6
- Co-authors
- Jon Paul Janet (5 shared papers)Ola Engkvist (5 shared papers)Philippe Schwaller (3 shared papers)Kostas Papadopoulos (4 shared papers)Christian Margreitter (4 shared papers)Atanas Patronov (4 shared papers)Tianfan Fu (1 shared paper)Yingheng Wang (1 shared paper)
- Journals
- Nature Machine Intelligence (3 papers)Chemical Science (2 papers)Nature Computational Science (1 paper)Genetics in Medicine (1 paper)Journal of Cheminformatics (1 paper)
- Partner nations
- SwedenSwitzerlandUnited Kingdom
In The Last Decade
Jeff Guo
8 papers receiving 230 citations
Jeff Guo's Hit Papers
Peers
Comparison fields: 5 of 65
- Computational Theory and Mathematics 133
- Materials Chemistry 126
- Molecular Biology 107
- Environmental Chemistry 14
- Biophysics 7
Countries citing papers authored by Jeff Guo
This map shows the geographic impact of Jeff Guo'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 Jeff Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Guo more than expected).
Fields of papers citing papers by Jeff Guo
This network shows the impact of papers produced by Jeff Guo. 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 Jeff Guo. The network helps show where Jeff Guo may publish in the future.
Co-authors
The 22 scholars most cited alongside Jeff Guo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Machine learning-aided generative molecular design Hit paper breakdown → | 2024 | 68 |
| 2 | 2023 | 48 | |
| 3 | 2021 | 39 | |
| 4 | 2022 | 33 | |
| 5 | 2024 | 22 | |
| 6 | 2024 | 13 | |
| 7 | 2025 | 9 | |
| 8 | 2022 | 1 | |
| 9 | 2026 | 0 | |
| 10 | 2025 | 0 |
About Jeff Guo
Jeff Guo is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Environmental Chemistry and Biomedical Engineering, having authored 10 papers that have together received 233 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (6 papers), Chemistry and Chemical Engineering (3 papers), Protein Structure and Dynamics (2 papers), Innovative Microfluidic and Catalytic Techniques Innovation (2 papers), Protein Degradation and Inhibitors (1 paper), Genomics and Rare Diseases (1 paper) and Nanowire Synthesis and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (133 citations), Materials Chemistry (126 citations), Molecular Biology (107 citations), Environmental Chemistry (14 citations) and Biophysics (7 citations). Jeff Guo has collaborated with scholars based in Sweden, Switzerland and United Kingdom. Frequent co-authors include Jon Paul Janet, Ola Engkvist, Philippe Schwaller, Kostas Papadopoulos, Christian Margreitter, Atanas Patronov, Tianfan Fu, Yingheng Wang, Arian R. Jamasb and Tom L. Blundell. Their work appears in journals such as Nature Machine Intelligence, Chemical Science, Nature Computational Science, Genetics in Medicine and Journal of Cheminformatics.
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.