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 7
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- Machine Learning in Materials Science 6
- Co-authors
- Philippe Schwaller (5 shared papers)Ola Engkvist (5 shared papers)Jon Paul Janet (5 shared papers)Christian Margreitter (4 shared papers)Kostas Papadopoulos (4 shared papers)Atanas Patronov (4 shared papers)Charles B. Harris (1 shared paper)Tianfan Fu (1 shared paper)
- Journals
- Nature Machine Intelligence (4 papers)Chemical Science (2 papers)Genetics in Medicine (1 paper)Journal of Cheminformatics (1 paper)Nature Computational Science (1 paper)
- Partner nations
- SwitzerlandSwedenUnited States
In The Last Decade
Jeff Guo
8 papers receiving 281 citations
Jeff Guo's Hit Papers
Peers
Comparison fields: 5 of 66
- Computational Theory and Mathematics 142
- Materials Chemistry 139
- Molecular Biology 117
- Environmental Chemistry 13
- Biophysics 6
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 23 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 | 89 |
| 2 | 2023 | 57 | |
| 3 | 2021 | 43 | |
| 4 | 2022 | 34 | |
| 5 | 2024 | 31 | |
| 6 | 2024 | 16 | |
| 7 | 2025 | 13 | |
| 8 | 2025 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2026 | 0 | |
| 11 | 2025 | 0 | |
| 12 | 2026 | 0 |
About Jeff Guo
Jeff Guo is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Environmental Chemistry, Molecular Biology and Biomedical Engineering, having authored 12 papers that have together received 285 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (6 papers), Chemistry and Chemical Engineering (4 papers), Innovative Microfluidic and Catalytic Techniques Innovation (2 papers), Protein Structure and Dynamics (2 papers), Genomics and Rare Diseases (1 paper), Nanowire Synthesis and Applications (1 paper) and Protein Degradation and Inhibitors (1 paper). The work is most often cited by research in Computational Theory and Mathematics (142 citations), Materials Chemistry (139 citations), Molecular Biology (117 citations), Environmental Chemistry (13 citations) and Biophysics (6 citations). Jeff Guo has collaborated with scholars based in Switzerland, Sweden and United States. Frequent co-authors include Philippe Schwaller, Ola Engkvist, Jon Paul Janet, Christian Margreitter, Kostas Papadopoulos, Atanas Patronov, Charles B. Harris, Tianfan Fu, Yingheng Wang and Yuanqi Du. Their work appears in journals such as Nature Machine Intelligence, Chemical Science, Genetics in Medicine, Journal of Cheminformatics and Nature Computational Science.
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.