Bharath Ramsundar
- Computational Theory and Mathematics top 0.1%
- Materials Chemistry top 2%
- Molecular Biology top 5%
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Co-authors
- Vijay S. PandeZhenqin WuJoseph GomesEvan N. FeinbergGreg S. CorradoClaire CuiJeff DeanKatherine Chou
- Topics
- Machine Learning in Materials Science (9 papers)Computational Drug Discovery Methods (8 papers)Protein Structure and Dynamics (6 papers)
- Partner nations
- United StatesGhanaHong Kong
In The Last Decade
Bharath Ramsundar
18 papers receiving 5.6k citations
Hit Papers
Peers
Comparison fields: 5 of 192
- Computational Theory and Mathematics 2.4k
- Materials Chemistry 2.0k
- Molecular Biology 1.9k
- Artificial Intelligence 1.4k
- Radiology, Nuclear Medicine and Imaging 787
Countries citing papers authored by Bharath Ramsundar
This map shows the geographic impact of Bharath Ramsundar'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 Bharath Ramsundar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bharath Ramsundar more than expected).
Fields of papers citing papers by Bharath Ramsundar
This network shows the impact of papers produced by Bharath Ramsundar. 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 Bharath Ramsundar. The network helps show where Bharath Ramsundar may publish in the future.
Co-authorship network of co-authors of Bharath Ramsundar
This figure shows the co-authorship network connecting the top 25 collaborators of Bharath Ramsundar. A scholar is included among the top collaborators of Bharath Ramsundar 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 Bharath Ramsundar. Bharath Ramsundar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 18 | |
| 4 | 0 | |
| 5 | 9 | |
| 6 | 28 | |
| 7 | A guide to deep learning in healthcarebreakdown → | 2350 |
| 8 | TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning | 53 |
| 9 | 298 | |
| 10 | Spatial Graph Convolutions for Drug Discovery | 2 |
| 11 | Low Data Drug Discovery with One-Shot Learningbreakdown → | 514 |
| 12 | MoleculeNet: a benchmark for molecular machine learningbreakdown → | 1696 |
| 13 | 183 | |
| 14 | Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Modelsbreakdown → | 358 |
| 15 | 207 | |
| 16 | 1 | |
| 17 | 2 | |
| 18 | NVMKV: a scalable and lightweight flash aware key-value store | 33 |
| 19 | 7 | |
| 20 | Dynamic Scaled Sampling for Deterministic Constraints | 4 |
About Bharath Ramsundar
Bharath Ramsundar is a scholar working on Health Informatics, Computational Theory and Mathematics and Materials Chemistry, having authored 21 papers that have together received 5.8k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (8 papers) and Protein Structure and Dynamics (6 papers). The work is most often cited by research in Health Informatics (658 citations), Computational Theory and Mathematics (2.4k citations) and Health Information Management (307 citations). Bharath Ramsundar has collaborated with scholars based in United States, Ghana and Hong Kong. Frequent co-authors include Vijay S. Pande, Zhenqin Wu, Joseph Gomes, Evan N. Feinberg, Greg S. Corrado, Claire Cui, Jeff Dean, Katherine Chou, Volodymyr Kuleshov and Sebastian Thrun. Their work appears in journals such as Nature Medicine, Nature Communications and Chemical 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.