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 →
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
2023282 citationsKyle Swanson, Eric Q. Wu et al.Cellprofile →
Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
2023183 citationsGary Liu, Denise B. Catacutan et al.Nature Chemical Biologyprofile →
ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries
202488 citationsKyle Swanson, Jeremy Leitz et al.Bioinformaticsprofile →
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics
202483 citationsKyle Swanson, Gary Liu et al.Nature Machine Intelligenceprofile →
The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies
202518 citationsKyle Swanson, Wesley Wu et al.Natureprofile →
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 Kyle Swanson'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 Kyle Swanson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Swanson more than expected).
This network shows the impact of papers produced by Kyle Swanson. 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 Kyle Swanson. The network helps show where Kyle Swanson may publish in the future.
Co-authorship network of co-authors of Kyle Swanson
This figure shows the co-authorship network connecting the top 25 collaborators of Kyle Swanson.
A scholar is included among the top collaborators of Kyle Swanson 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 Kyle Swanson. Kyle Swanson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Swanson, Kyle, et al.. (2025). The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies. Nature. 646(8085). 716–723.18 indexed citations breakdown →
2.
Swanson, Kyle, Jeremy Leitz, Souhrid Mukherjee, et al.. (2024). ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics. 40(7).88 indexed citations breakdown →
Swanson, Kyle, Eric Q. Wu, Angela Zhang, Ash A. Alizadeh, & James Zou. (2023). From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell. 186(8). 1772–1791.282 indexed citations breakdown →
10.
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 →
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 →
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.