Wengong Jin
- Computational Theory and Mathematics top 0.1%
- Molecular Biology top 5%
- Materials Chemistry top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering top 10%
- Co-authors
- Tommi JaakkolaRegina BarzilayKlavs F. JensenKyle SwansonKevin YangConnor W. ColeyJames J. CollinsJonathan Stokes
- Topics
- Computational Drug Discovery Methods (17 papers)Machine Learning in Materials Science (13 papers)Protein Structure and Dynamics (4 papers)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Wengong Jin
23 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 175
- Computational Theory and Mathematics 2.0k
- Molecular Biology 1.6k
- Materials Chemistry 1.6k
- Artificial Intelligence 387
- Biomedical Engineering 278
Countries citing papers authored by Wengong Jin
This map shows the geographic impact of Wengong Jin'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 Wengong Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wengong Jin more than expected).
Fields of papers citing papers by Wengong Jin
This network shows the impact of papers produced by Wengong Jin. 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 Wengong Jin. The network helps show where Wengong Jin may publish in the future.
Co-authorship network of co-authors of Wengong Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Wengong Jin. A scholar is included among the top collaborators of Wengong Jin 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 Wengong Jin. Wengong Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 0 | |
| 3 | 20 | |
| 4 | Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumanniibreakdown → | 183 |
| 5 | 4 | |
| 6 | 89 | |
| 7 | Generative models for molecular discovery: Recent advances and challengesbreakdown → | 162 |
| 8 | 114 | |
| 9 | 8 | |
| 10 | 11 | |
| 11 | Multi-Objective Molecule Generation using Interpretable Substructures | 3 |
| 12 | Composing Molecules with Multiple Property Constraints | 3 |
| 13 | A Deep Learning Approach to Antibiotic Discoverybreakdown → | 1298 |
| 14 | 25 | |
| 15 | Analyzing Learned Molecular Representations for Property Predictionbreakdown → | 1081 |
| 16 | 2 | |
| 17 | A graph-convolutional neural network model for the prediction of chemical reactivitybreakdown → | 460 |
| 18 | Junction Tree Variational Autoencoder for Molecular Graph Generation | 39 |
| 19 | Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. | 15 |
| 20 | Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network | 11 |
About Wengong Jin
Wengong Jin is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Medicine, having authored 24 papers that have together received 3.6k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (13 papers) and Protein Structure and Dynamics (4 papers). The work is most often cited by research in Computational Theory and Mathematics (2.0k citations), Health Informatics (57 citations) and Molecular Medicine (180 citations). Wengong Jin has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Tommi Jaakkola, Regina Barzilay, Klavs F. Jensen, Kyle Swanson, Kevin Yang, Connor W. Coley, James J. Collins, Jonathan Stokes, Anush Chiappino-Pepe and Brian Kelley. Their work appears in journals such as Science, Cell and Proceedings of the National Academy of Sciences.
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.