Marwin Segler
Impact in
- Computational Theory and Mathematics top 0.1%
- Computational Drug Discovery Methods
- Materials Chemistry top 2%
- Machine Learning in Materials Science
Papers in
-
- Computational Drug Discovery Methods 16
-
- Machine Learning in Materials Science 19
- Co-authors
- Mark P. WallerMike PreußChristian TyrchanThierry KogejFrederik SandfortFelix Strieth‐KalthoffFrank GloriusMohamed A. Ahmed
- Journals
- Journal of Chemical Information and Modeling (3 papers)ACS Central Science (2 papers)Chemistry - A European Journal (2 papers)Information Sciences (1 paper)Nature Communications (1 paper)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Marwin Segler
20 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Computational Theory and Mathematics 2.0k
- Materials Chemistry 1.9k
- Health Informatics 51
- Molecular Biology 1.3k
- Biophysics 84
Countries citing papers authored by Marwin Segler
This map shows the geographic impact of Marwin Segler'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 Marwin Segler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marwin Segler more than expected).
Fields of papers citing papers by Marwin Segler
This network shows the impact of papers produced by Marwin Segler. 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 Marwin Segler. The network helps show where Marwin Segler may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Marwin Segler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 4 | |
| 3 | 2024 | 18 | |
| 4 | 2023 | 9 | |
| 5 | 2023 | 13 | |
| 6 | 2023 | 26 | |
| 7 | 2022 | 90 | |
| 8 | 2022 | 26 | |
| 9 | FS-Mol: A Few-Shot Learning Dataset of Molecules | 2021 | 4 |
| 10 | 2020 | 207 | |
| 11 | 2020 | 8 | |
| 12 | Generating molecules via chemical reactions | 2019 | 1 |
| 13 | 2019 | 26 | |
| 14 | Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design | 2018 | 35 |
| 15 | Planning chemical syntheses with deep neural networks and symbolic AI Hit paper breakdown → | 2018 | 1211 |
| 16 | 2018 | 85 | |
| 17 | Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction Hit paper breakdown → | 2017 | 368 |
| 18 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks Hit paper breakdown → | 2017 | 969 |
| 19 | 2015 | 21 | |
| 20 | 2011 | 27 |
About Marwin Segler
Marwin Segler is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Information Systems and Management, Artificial Intelligence and Molecular Biology, having authored 21 papers that have together received 3.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (19 papers), Computational Drug Discovery Methods (16 papers), Chemical Synthesis and Analysis (4 papers), Innovative Microfluidic and Catalytic Techniques Innovation (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Catalytic C–H Functionalization Methods (2 papers), Machine Learning and Data Classification (2 papers) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (2.0k citations), Materials Chemistry (1.9k citations), Health Informatics (51 citations), Molecular Biology (1.3k citations) and Biophysics (84 citations). Marwin Segler has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Mark P. Waller, Mike Preuß, Christian Tyrchan, Thierry Kogej, Frederik Sandfort, Felix Strieth‐Kalthoff, Frank Glorius, Mohamed A. Ahmed, Nathan Brown and Nadine Schneider. Their work appears in journals such as Journal of Chemical Information and Modeling, ACS Central Science, Chemistry - A European Journal, Information Sciences and Nature Communications.
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.