Payel Das
Impact in
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- Computational Drug Discovery Methods
- Molecular Biology top 10%
- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
Papers in
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- Computational Drug Discovery Methods 16
- Co-authors
- Ruhong ZhouAnimesh DebnathCecilia ClementiLydia E. KavrakiVijil ChenthamarakshanSilvina MatysiakYoussef MrouehMark Moll
- Journals
- The Journal of Physical Chemistry B (9 papers)Proceedings of the National Academy of Sciences (5 papers)Nature Machine Intelligence (5 papers)Biophysical Journal (4 papers)Journal of the American Chemical Society (3 papers)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Payel Das
78 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Computational Theory and Mathematics 405
- Molecular Biology 1.2k
- Materials Chemistry 718
- Health Informatics 18
- Water Science and Technology 180
Countries citing papers authored by Payel Das
This map shows the geographic impact of Payel Das'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 Payel Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Payel Das more than expected).
Fields of papers citing papers by Payel Das
This network shows the impact of papers produced by Payel Das. 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 Payel Das. The network helps show where Payel Das may publish in the future.
Co-authors
The 25 scholars most cited alongside Payel Das, 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 | 3 | |
| 2 | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance Hit paper breakdown → | 2025 | 33 |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 30 | |
| 6 | 2023 | 51 | |
| 7 | 2023 | 22 | |
| 8 | 2023 | 6 | |
| 9 | 2022 | 5 | |
| 10 | 2021 | 12 | |
| 11 | 2020 | 33 | |
| 12 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models | 2020 | 30 |
| 13 | Interactive Visual Exploration of Latent Space (IVELS) for Peptide Auto-Encoder Model Selection | 2019 | 3 |
| 14 | Comparative study of selected hysiologicaland bio-chemical variables during different phases of menstruation | 2017 | 0 |
| 15 | 2017 | 7 | |
| 16 | 2016 | 3 | |
| 17 | 2015 | 48 | |
| 18 | 2014 | 41 | |
| 19 | 2012 | 5 | |
| 20 | 2012 | 8 |
About Payel Das
Payel Das is a scholar working on Computational Theory and Mathematics, Structural Biology, Molecular Biology, Museology and Artificial Intelligence, having authored 86 papers that have together received 2.3k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (24 papers), Computational Drug Discovery Methods (16 papers), Enzyme Structure and Function (11 papers), Machine Learning in Materials Science (7 papers), Alzheimer's disease research and treatments (7 papers), Nanomaterials for catalytic reactions (6 papers), Topic Modeling (5 papers) and Adsorption and biosorption for pollutant removal (5 papers). The work is most often cited by research in Computational Theory and Mathematics (405 citations), Molecular Biology (1.2k citations), Materials Chemistry (718 citations), Health Informatics (18 citations) and Water Science and Technology (180 citations). Payel Das has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Ruhong Zhou, Animesh Debnath, Cecilia Clementi, Lydia E. Kavraki, Vijil Chenthamarakshan, Silvina Matysiak, Youssef Mroueh, Mark Moll, Inkit Padhi and Georges Belfort. Their work appears in journals such as The Journal of Physical Chemistry B, Proceedings of the National Academy of Sciences, Nature Machine Intelligence, Biophysical Journal and Journal of the American Chemical Society.
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.