Arip Asadulaev
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Protein Structure and Dynamics 2
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- Anomaly Detection Techniques and Applications 1
- Reinforcement Learning in Robotics 1
- Machine Learning and ELM 1
- Data Stream Mining Techniques 1
- Co-authors
- Evgeny Putin (2 shared papers)Yan A. Ivanenkov (2 shared papers)Alex Zhavoronkov (2 shared papers)Vladimir Aladinskiy (1 shared paper)Benjamín Sánchez-Lengeling (1 shared paper)Alán Aspuru‐Guzik (1 shared paper)Alex Aliper (1 shared paper)Quentin Vanhaelen (1 shared paper)
- Journals
- Molecular Pharmaceutics (1 paper)Journal of Chemical Information and Modeling (1 paper)IEEE Access (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- RussiaFinlandUnited States
In The Last Decade
Arip Asadulaev
5 papers receiving 399 citations
Peers
Comparison fields: 5 of 71
- Computational Theory and Mathematics 313
- Materials Chemistry 247
- Biophysics 26
- Health Informatics 5
- Molecular Biology 197
Countries citing papers authored by Arip Asadulaev
This map shows the geographic impact of Arip Asadulaev'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 Arip Asadulaev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arip Asadulaev more than expected).
Fields of papers citing papers by Arip Asadulaev
This network shows the impact of papers produced by Arip Asadulaev. 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 Arip Asadulaev. The network helps show where Arip Asadulaev may publish in the future.
Co-authors
The 13 scholars most cited alongside Arip Asadulaev, 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 | 2018 | 260 | |
| 2 | 2018 | 156 | |
| 3 | 2020 | 5 | |
| 4 | Linear Distillation Learning. | 2019 | 1 |
| 5 | 2021 | 1 |
About Arip Asadulaev
Arip Asadulaev is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Materials Chemistry and Control and Systems Engineering, having authored 5 papers that have together received 423 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (2 papers), Computational Drug Discovery Methods (2 papers), Machine Learning in Materials Science (2 papers), Anomaly Detection Techniques and Applications (1 paper), Fault Detection and Control Systems (1 paper), Reinforcement Learning in Robotics (1 paper), Machine Learning and ELM (1 paper) and Data Stream Mining Techniques (1 paper). The work is most often cited by research in Computational Theory and Mathematics (313 citations), Materials Chemistry (247 citations), Biophysics (26 citations), Health Informatics (5 citations) and Molecular Biology (197 citations). Arip Asadulaev has collaborated with scholars based in Russia, Finland and United States. Frequent co-authors include Evgeny Putin, Yan A. Ivanenkov, Alex Zhavoronkov, Vladimir Aladinskiy, Benjamín Sánchez-Lengeling, Alán Aspuru‐Guzik, Alex Aliper, Quentin Vanhaelen, Anastasia V. Aladinskaya and Andrey Filchenkov. Their work appears in journals such as Molecular Pharmaceutics, Journal of Chemical Information and Modeling, IEEE Access and arXiv (Cornell University).
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.