Rishal Aggarwal
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Genetics, Bioinformatics, and Biomedical Research
- Chemical Synthesis and Analysis
Papers in
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- Computational Drug Discovery Methods 6
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- Protein Structure and Dynamics 4
- vaccines and immunoinformatics approaches 2
- Co-authors
- U. Deva Priyakumar (4 shared papers)P. K. Vinod (1 shared paper)David Ryan Koes (2 shared papers)Andrew T. McNutt (2 shared papers)Rocco Meli (2 shared papers)Jocelyn Sunseri (1 shared paper)Paul Francoeur (1 shared paper)Matthew Ragoza (1 shared paper)
- Journals
- Journal of Cheminformatics (2 papers)Journal of Chemical Information and Modeling (2 papers)ACS Omega (1 paper)Wiley Interdisciplinary Reviews Computational Molecular Science (1 paper)
- Partner nations
- IndiaUnited KingdomUnited States
In The Last Decade
Rishal Aggarwal
6 papers receiving 782 citations
Rishal Aggarwal's Hit Papers
Peers
Comparison fields: 5 of 111
- Computational Theory and Mathematics 498
- Molecular Biology 505
- Materials Chemistry 274
- Health Informatics 5
- Pharmacology 58
Countries citing papers authored by Rishal Aggarwal
This map shows the geographic impact of Rishal Aggarwal'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 Rishal Aggarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishal Aggarwal more than expected).
Fields of papers citing papers by Rishal Aggarwal
This network shows the impact of papers produced by Rishal Aggarwal. 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 Rishal Aggarwal. The network helps show where Rishal Aggarwal may publish in the future.
Co-authors
The 13 scholars most cited alongside Rishal Aggarwal, 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 | GNINA 1.0: molecular docking with deep learning Hit paper breakdown → | 2021 | 397 |
| 2 | MolGPT: Molecular Generation Using a Transformer-Decoder Model Hit paper breakdown → | 2021 | 282 |
| 3 | 2021 | 74 | |
| 4 | GNINA 1.3: the next increment in molecular docking with deep learning Hit paper breakdown → | 2025 | 19 |
| 5 | 2022 | 15 | |
| 6 | 2023 | 12 |
About Rishal Aggarwal
Rishal Aggarwal is a scholar working on Computational Theory and Mathematics, Molecular Biology, Materials Chemistry, Organic Chemistry and Infectious Diseases, having authored 6 papers that have together received 799 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Protein Structure and Dynamics (4 papers), Machine Learning in Materials Science (3 papers), Click Chemistry and Applications (2 papers), vaccines and immunoinformatics approaches (2 papers) and Synthesis and biological activity (1 paper). The work is most often cited by research in Computational Theory and Mathematics (498 citations), Molecular Biology (505 citations), Materials Chemistry (274 citations), Health Informatics (5 citations) and Pharmacology (58 citations). Rishal Aggarwal has collaborated with scholars based in India, United Kingdom and United States. Frequent co-authors include U. Deva Priyakumar, P. K. Vinod, David Ryan Koes, Andrew T. McNutt, Rocco Meli, Jocelyn Sunseri, Paul Francoeur, Matthew Ragoza, Tomohide Masuda and Akash Gupta. Their work appears in journals such as Journal of Cheminformatics, Journal of Chemical Information and Modeling, ACS Omega and Wiley Interdisciplinary Reviews Computational Molecular Science.
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.