Jon Paul Janet

3.6k total citations · 2 hit papers
45 papers, 2.2k citations indexed

About

Jon Paul Janet is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Jon Paul Janet has authored 45 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Materials Chemistry, 26 papers in Computational Theory and Mathematics and 19 papers in Molecular Biology. Recurrent topics in Jon Paul Janet's work include Machine Learning in Materials Science (30 papers), Computational Drug Discovery Methods (26 papers) and Protein Structure and Dynamics (14 papers). Jon Paul Janet is often cited by papers focused on Machine Learning in Materials Science (30 papers), Computational Drug Discovery Methods (26 papers) and Protein Structure and Dynamics (14 papers). Jon Paul Janet collaborates with scholars based in Sweden, United States and United Kingdom. Jon Paul Janet's co-authors include Heather J. Kulik, Aditya Nandy, Chenru Duan, Tzuhsiung Yang, Ola Engkvist, Yongjin Lee, Seyed Mohamad Moosavi, Berend Smit, Peter G. Boyd and Kevin Maik Jablonka and has published in prestigious journals such as Angewandte Chemie International Edition, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Jon Paul Janet

41 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

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.

Understanding the diversity of the metal-organic framework ecosystem

2020 426 citations Seyed Mohamad Moosavi, Aditya Nandy et al. Nature Communications profile →
Reinvent 4: Modern AI–driven generative molecule design

2024 104 citations Hannes H. Loeffler, Jiazhen He et al. Journal of Cheminformatics profile →

Peers

Jon Paul Janet

MC

CTM

IC

MB

RESE

Aditya Nandy United States

Chenru Duan United States

Philippe Schwaller Switzerland

Florian Häse Canada

Yi‐Pei Li Taiwan

Loı̈c M. Roch Switzerland

Rohit Batra United States

Cheng Shang China

Rocío Mercado Sweden

Simon Batzner United States

Aditya Nandy United States

Jon Paul Janet

1.6k ×1.0

MC

462 ×0.6

CTM

855 ×1.6

IC

231 ×0.6

MB

312 ×1.3

RESE

Citations per year, relative to Jon Paul Janet Jon Paul Janet (= 1×) peers Aditya Nandy

Countries citing papers authored by Jon Paul Janet

Since Specialization

Citations

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

Fields of papers citing papers by Jon Paul Janet

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jon Paul Janet. 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 Jon Paul Janet. The network helps show where Jon Paul Janet may publish in the future.

Co-authorship network of co-authors of Jon Paul Janet

This figure shows the co-authorship network connecting the top 25 collaborators of Jon Paul Janet. A scholar is included among the top collaborators of Jon Paul Janet 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 Jon Paul Janet. Jon Paul Janet is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Lewis, Richard J., et al.. (2025). Advancing Structure Elucidation with a Flexible Multi‐Spectral AI Model. Angewandte Chemie International Edition. 65(2). e17611–e17611.

2.

Guo, Jeff, et al.. (2024). Sample efficient reinforcement learning with active learning for molecular design. Chemical Science. 15(11). 4146–4160. 22 indexed citations

3.

Tibo, Alessandro, Jiazhen He, Jon Paul Janet, Eva Nittinger, & Ola Engkvist. (2024). Exhaustive local chemical space exploration using a transformer model. Nature Communications. 15(1). 7315–7315. 9 indexed citations

4.

Löhr, Thomas, et al.. (2024). Navigating the Maize: cyclic and conditional computational graphs for molecular simulation. Digital Discovery. 3(12). 2551–2559.

5.

He, Jiazhen, Alessandro Tibo, Jon Paul Janet, et al.. (2024). Evaluation of reinforcement learning in transformer-based molecular design. Journal of Cheminformatics. 16(1). 95–95. 5 indexed citations

6.

Janet, Jon Paul, et al.. (2024). Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. Nature Communications. 15(1). 1517–1517. 52 indexed citations

7.

Heinonen, Markus, Mikhail Kabeshov, Jon Paul Janet, et al.. (2024). Human-in-the-loop active learning for goal-oriented molecule generation. Journal of Cheminformatics. 16(1). 138–138. 7 indexed citations

8.

Loeffler, Hannes H., Jiazhen He, Alessandro Tibo, et al.. (2024). Reinvent 4: Modern AI–driven generative molecule design. Journal of Cheminformatics. 16(1). 20–20. 104 indexed citations breakdown →

9.

Lewis, Richard J., et al.. (2024). HSQC Spectra Simulation and Matching for Molecular Identification. Journal of Chemical Information and Modeling. 64(8). 3180–3191. 4 indexed citations

10.

Sala, Giuseppina La, Christopher Pfleger, Helena Käck, et al.. (2023). Combining structural and coevolution information to unveil allosteric sites. Chemical Science. 14(25). 7057–7067. 13 indexed citations

11.

Moore, J. Harry, Christian Margreitter, Jon Paul Janet, et al.. (2023). Automated relative binding free energy calculations from SMILES to ΔΔG. Communications Chemistry. 6(1). 82–82. 11 indexed citations

12.

Janet, Jon Paul, Lewis Mervin, & Ola Engkvist. (2023). Artificial intelligence in molecular de novo design: Integration with experiment. Current Opinion in Structural Biology. 80. 102575–102575. 15 indexed citations

13.

Balakrishnan, S., et al.. (2023). Online Complaint Management System using Image Recognition. 1389–1393.

14.

Nandy, Aditya, et al.. (2022). Representations and strategies for transferable machine learning improve model performance in chemical discovery. The Journal of Chemical Physics. 156(7). 74101–74101. 12 indexed citations

15.

Sujatha, K., et al.. (2022). A hybrid discriminant fuzzy DNN with enhanced modularity bat algorithm for speech recognition. Journal of Intelligent & Fuzzy Systems. 44(3). 4079–4091. 3 indexed citations

16.

Guo, Jeff, Jon Paul Janet, Matthias R. Bauer, et al.. (2021). DockStream: a docking wrapper to enhance de novo molecular design. Journal of Cheminformatics. 13(1). 89–89. 39 indexed citations

17.

Papadopoulos, Kostas, et al.. (2021). De novo design with deep generative models based on 3D similarity scoring. Bioorganic & Medicinal Chemistry. 44. 116308–116308. 18 indexed citations

18.

Moosavi, Seyed Mohamad, Aditya Nandy, Kevin Maik Jablonka, et al.. (2020). Understanding the diversity of the metal-organic framework ecosystem. Nature Communications. 11(1). 4068–4068. 426 indexed citations breakdown →

19.

Janet, Jon Paul, et al.. (2019). Enumeration of de novo inorganic complexes for chemical discovery and machine learning. Molecular Systems Design & Engineering. 5(1). 139–152. 32 indexed citations

20.

Balakrishnan, Suresh, Jon Paul Janet, K. Sujatha, & S. Sheeba Rani. (2018). An efficient and complete automatic system for detecting of lung module. Indian Journal of Science and Technology. 11(26). 1–5. 3 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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