Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Deep Learning Approach to Antibiotic Discovery
20201.3k citationsJonathan Stokes, Kevin Yang et al.Cellprofile →
Analyzing Learned Molecular Representations for Property Prediction
20191.1k citationsKevin Yang, Kyle Swanson et al.Journal of Chemical Information and Modelingprofile →
A graph-convolutional neural network model for the prediction of chemical reactivity
2018460 citationsConnor W. Coley, Wengong Jin et al.Chemical Scienceprofile →
Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
2023183 citationsGary Liu, Denise B. Catacutan et al.Nature Chemical Biologyprofile →
Generative models for molecular discovery: Recent advances and challenges
2022162 citationsCamille Bilodeau, Wengong Jin et al.Wiley Interdisciplinary Reviews Computational Molecular Scienceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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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).
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.
Liu, Gary, Denise B. Catacutan, Kyle Swanson, et al.. (2023). Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nature Chemical Biology. 19(11). 1342–1350.183 indexed citations breakdown →
Jin, Wengong, et al.. (2020). Multi-Objective Molecule Generation using Interpretable Substructures. International Conference on Machine Learning. 4849–4859.3 indexed citations
12.
Jin, Wengong, Regina Barzilay, & Tommi Jaakkola. (2020). Composing Molecules with Multiple Property Constraints. 1.3 indexed citations
13.
Stokes, Jonathan, Kevin Yang, Kyle Swanson, et al.. (2020). A Deep Learning Approach to Antibiotic Discovery. Cell. 180(4). 688–702.e13.1298 indexed citations breakdown →
Yang, Kevin, Kyle Swanson, Wengong Jin, et al.. (2019). Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling. 59(8). 3370–3388.1081 indexed citations breakdown →
Coley, Connor W., Wengong Jin, Luke Rogers, et al.. (2018). A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science. 10(2). 370–377.460 indexed citations breakdown →
18.
Jin, Wengong, Regina Barzilay, & Tommi Jaakkola. (2018). Junction Tree Variational Autoencoder for Molecular Graph Generation. DSpace@MIT (Massachusetts Institute of Technology). 2323–2332.39 indexed citations
19.
Jin, Wengong, Kevin Yang, Regina Barzilay, & Tommi Jaakkola. (2018). Learning Multimodal Graph-to-Graph Translation for Molecule Optimization.. arXiv (Cornell University).15 indexed citations
20.
Jin, Wengong, Connor W. Coley, Regina Barzilay, & Tommi Jaakkola. (2017). Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. DSpace@MIT (Massachusetts Institute of Technology). 30. 2607–2616.11 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.