Kenneth Atz

1.3k total citations
22 papers, 646 citations indexed

About

Kenneth Atz is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Kenneth Atz has authored 22 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 16 papers in Computational Theory and Mathematics and 12 papers in Materials Chemistry. Recurrent topics in Kenneth Atz's work include Computational Drug Discovery Methods (16 papers), Machine Learning in Materials Science (11 papers) and Protein Structure and Dynamics (7 papers). Kenneth Atz is often cited by papers focused on Computational Drug Discovery Methods (16 papers), Machine Learning in Materials Science (11 papers) and Protein Structure and Dynamics (7 papers). Kenneth Atz collaborates with scholars based in Switzerland, Germany and Singapore. Kenneth Atz's co-authors include Gisbert Schneider, Clemens Isert, José Jiménez-Luna, Michaël Moret, Francesca Grisoni, Daniel Merk, Alexander L. Button, Leandro Cotos, Uwe Grether and Martin Baumgartner and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and Nature Chemistry.

In The Last Decade

Kenneth Atz

22 papers receiving 639 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kenneth Atz Switzerland 11 371 309 300 104 67 22 646
Barbara Mikulak-Klucznik South Korea 8 286 0.8× 275 0.9× 325 1.1× 78 0.8× 140 2.1× 9 676
Jack L. Sloane United States 9 247 0.7× 265 0.9× 245 0.8× 108 1.0× 65 1.0× 10 580
Tomasz Badowski Poland 9 261 0.7× 208 0.7× 318 1.1× 72 0.7× 89 1.3× 10 523
James L. Melville United Kingdom 15 422 1.1× 378 1.2× 171 0.6× 130 1.3× 62 0.9× 23 775
Michał D. Bajczyk Poland 7 283 0.8× 224 0.7× 293 1.0× 49 0.5× 113 1.7× 9 520
Christof Gerlach Germany 9 384 1.0× 421 1.4× 257 0.9× 91 0.9× 29 0.4× 15 704
Tomohide Masuda Japan 6 476 1.3× 495 1.6× 246 0.8× 79 0.8× 34 0.5× 10 744
Paul Francoeur United States 7 449 1.2× 468 1.5× 223 0.7× 57 0.5× 29 0.4× 10 702
Agnieszka Wołos Poland 9 208 0.6× 224 0.7× 259 0.9× 91 0.9× 116 1.7× 16 601
Jike Wang China 18 753 2.0× 660 2.1× 458 1.5× 57 0.5× 79 1.2× 52 1.1k

Countries citing papers authored by Kenneth Atz

Since Specialization
Citations

This map shows the geographic impact of Kenneth Atz'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 Kenneth Atz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kenneth Atz more than expected).

Fields of papers citing papers by Kenneth Atz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kenneth Atz. 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 Kenneth Atz. The network helps show where Kenneth Atz may publish in the future.

Co-authorship network of co-authors of Kenneth Atz

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth Atz. A scholar is included among the top collaborators of Kenneth Atz 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 Kenneth Atz. Kenneth Atz 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.
Albrecht, Manuela, et al.. (2025). Discovery of 1,3,4‐Oxadiazin‐5‐One Derivative CJ1‐34 as a Partial ATP Synthase Inhibitor for CNS Applications. Chemistry - A European Journal. 31(24). e202404517–e202404517. 1 indexed citations
2.
Müller, Alex T., Kenneth Atz, David B. Konrad, et al.. (2025). Simple User‐Friendly Reaction Format. Molecular Informatics. 44(1). e202400361–e202400361. 1 indexed citations
3.
Isert, Clemens, Kenneth Atz, Sereina Riniker, & Gisbert Schneider. (2024). Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning. RSC Advances. 14(7). 4492–4502. 11 indexed citations
4.
Atz, Kenneth, Leandro Cotos, Clemens Isert, et al.. (2024). Prospective de novo drug design with deep interactome learning. Nature Communications. 15(1). 3408–3408. 44 indexed citations
5.
Atz, Kenneth, Alex T. Müller, Andrea Anelli, et al.. (2024). Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry. RSC Medicinal Chemistry. 15(7). 2310–2321. 4 indexed citations
6.
Atz, Kenneth, et al.. (2024). G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction. Computational and Structural Biotechnology Journal. 23. 2872–2882. 3 indexed citations
7.
Atz, Kenneth, et al.. (2024). Combining de novo molecular design with semiempirical protein–ligand binding free energy calculation. RSC Advances. 14(50). 37035–37044. 1 indexed citations
8.
Atz, Kenneth, et al.. (2024). Protein Binding Site Representation in Latent Space. Molecular Informatics. 44(1). e202400205–e202400205. 1 indexed citations
9.
Atz, Kenneth, Alex T. Müller, Georg Wuitschik, et al.. (2023). Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning. Nature Chemistry. 16(2). 239–248. 50 indexed citations
10.
Atz, Kenneth, Alex T. Müller, Clemens Isert, et al.. (2023). Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening. Communications Chemistry. 6(1). 256–256. 8 indexed citations
11.
Moret, Michaël, Leandro Cotos, Kenneth Atz, et al.. (2023). Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nature Communications. 14(1). 114–114. 95 indexed citations
12.
Isert, Clemens, Kenneth Atz, & Gisbert Schneider. (2023). Structure-based drug design with geometric deep learning. Current Opinion in Structural Biology. 79. 102548–102548. 90 indexed citations
13.
Schneider, Gisbert, Werner Breitenstein, Jamal Bouitbir, et al.. (2023). Allosteric targeting resolves limitations of earlier LFA-1 directed modalities. Biochemical Pharmacology. 211. 115504–115504. 2 indexed citations
14.
Atz, Kenneth, Wolfgang Guba, Uwe Grether, & Gisbert Schneider. (2022). Machine Learning and Computational Chemistry for the Endocannabinoid System. Methods in molecular biology. 2576. 477–493. 3 indexed citations
15.
Isert, Clemens, Kenneth Atz, José Jiménez-Luna, & Gisbert Schneider. (2022). QMugs, quantum mechanical properties of drug-like molecules. Scientific Data. 9(1). 273–273. 92 indexed citations
16.
Isert, Clemens, et al.. (2022). Translating from Proteins to Ribonucleic Acids for Ligand‐binding Site Detection. Molecular Informatics. 41(10). e2200059–e2200059. 6 indexed citations
17.
Atz, Kenneth, et al.. (2022). Δ-Quantum machine-learning for medicinal chemistry. Physical Chemistry Chemical Physics. 24(18). 10775–10783. 42 indexed citations
18.
Grisoni, Francesca, Alexander L. Button, Michaël Moret, et al.. (2021). Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Science Advances. 7(24). 103 indexed citations
19.
Müntener, Thomas, Raphael Böhm, Kenneth Atz, Daniel Häußinger, & Sebastian Hiller. (2020). NMR pseudocontact shifts in a symmetric protein homotrimer. Journal of Biomolecular NMR. 74(8-9). 413–419. 6 indexed citations
20.
Haider, Ahmed, Julian Kretz, Luca Gobbi, et al.. (2019). Structure–Activity Relationship Studies of Pyridine-Based Ligands and Identification of a Fluorinated Derivative for Positron Emission Tomography Imaging of Cannabinoid Type 2 Receptors. Journal of Medicinal Chemistry. 62(24). 11165–11181. 18 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.

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