Rishal Aggarwal

1.6k total citations · 3 hit papers
6 papers, 799 citations indexed

About

Rishal Aggarwal is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Rishal Aggarwal has authored 6 papers receiving a total of 799 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computational Theory and Mathematics, 5 papers in Molecular Biology and 3 papers in Materials Chemistry. Recurrent topics in Rishal Aggarwal's work include Computational Drug Discovery Methods (6 papers), Protein Structure and Dynamics (4 papers) and Machine Learning in Materials Science (3 papers). Rishal Aggarwal is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Protein Structure and Dynamics (4 papers) and Machine Learning in Materials Science (3 papers). Rishal Aggarwal collaborates with scholars based in India, United Kingdom and United States. Rishal Aggarwal's co-authors include U. Deva Priyakumar, P. K. Vinod, Andrew T. McNutt, Rocco Meli, David Ryan Koes, Tomohide Masuda, Matthew Ragoza, Paul Francoeur, Jocelyn Sunseri and C. V. Jawahar and has published in prestigious journals such as Journal of Chemical Information and Modeling, ACS Omega and Wiley Interdisciplinary Reviews Computational Molecular Science.

In The Last Decade

Rishal Aggarwal

6 papers receiving 782 citations

Hit Papers

GNINA 1.0: molecular docking with deep learning 2021 2026 2022 2024 2021 2021 2025 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. GNINA 1.0: molecular docking with deep learning
    2021 397 citations Andrew T. McNutt, Paul Francoeur et al. Journal of Cheminformatics profile →
  2. MolGPT: Molecular Generation Using a Transformer-Decoder Model
    2021 282 citations Rishal Aggarwal, P. K. Vinod et al. Journal of Chemical Information and Modeling profile →
  3. GNINA 1.3: the next increment in molecular docking with deep learning
    2025 19 citations Andrew T. McNutt, Yanjing Li et al. Journal of Cheminformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rishal Aggarwal India 6 505 498 274 58 58 6 799
Paul Francoeur United States 7 468 0.9× 449 0.9× 223 0.8× 58 1.0× 57 1.0× 10 702
Ryan Byrne Switzerland 4 331 0.7× 382 0.8× 193 0.7× 57 1.0× 58 1.0× 8 692
Tomohide Masuda Japan 6 495 1.0× 476 1.0× 246 0.9× 67 1.2× 79 1.4× 10 744
Stefano Rensi United States 7 375 0.7× 493 1.0× 201 0.7× 62 1.1× 55 0.9× 10 804
Matthew Ragoza United States 5 392 0.8× 372 0.7× 176 0.6× 56 1.0× 57 1.0× 6 599
Wen Torng United States 7 632 1.3× 715 1.4× 337 1.2× 74 1.3× 70 1.2× 7 1.0k
Ahmet Süreyya Rifaioğlu Türkiye 8 672 1.3× 529 1.1× 174 0.6× 55 0.9× 29 0.5× 15 896
Nils Weskamp Germany 14 603 1.2× 587 1.2× 216 0.8× 96 1.7× 60 1.0× 21 905
Tunca Doğan Türkiye 13 859 1.7× 615 1.2× 220 0.8× 65 1.1× 34 0.6× 28 1.2k
Eloy Félix United Kingdom 6 545 1.1× 625 1.3× 230 0.8× 88 1.5× 68 1.2× 11 1.0k

Countries citing papers authored by Rishal Aggarwal

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Rishal Aggarwal

This figure shows the co-authorship network connecting the top 25 collaborators of Rishal Aggarwal. A scholar is included among the top collaborators of Rishal Aggarwal 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 Rishal Aggarwal. Rishal Aggarwal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

6 of 6 papers shown
1.
McNutt, Andrew T., Yanjing Li, Rocco Meli, Rishal Aggarwal, & David Ryan Koes. (2025). GNINA 1.3: the next increment in molecular docking with deep learning. Journal of Cheminformatics. 17(1). 28–28. 19 indexed citations breakdown →
2.
Aggarwal, Rishal, et al.. (2023). Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets. ACS Omega. 8(2). 2389–2397. 12 indexed citations
3.
Aggarwal, Rishal, et al.. (2022). Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods. Wiley Interdisciplinary Reviews Computational Molecular Science. 13(2). 15 indexed citations
4.
McNutt, Andrew T., Paul Francoeur, Rishal Aggarwal, et al.. (2021). GNINA 1.0: molecular docking with deep learning. Journal of Cheminformatics. 13(1). 43–43. 397 indexed citations breakdown →
5.
Aggarwal, Rishal, et al.. (2021). MolGPT: Molecular Generation Using a Transformer-Decoder Model. Journal of Chemical Information and Modeling. 62(9). 2064–2076. 282 indexed citations breakdown →
6.
Aggarwal, Rishal, et al.. (2021). DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks. Journal of Chemical Information and Modeling. 62(21). 5069–5079. 74 indexed citations

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.

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