Stefano Forli

17.3k total citations · 8 hit papers
96 papers, 10.7k citations indexed

About

Stefano Forli is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Stefano Forli has authored 96 papers receiving a total of 10.7k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Molecular Biology, 31 papers in Computational Theory and Mathematics and 18 papers in Organic Chemistry. Recurrent topics in Stefano Forli's work include Computational Drug Discovery Methods (31 papers), Protein Structure and Dynamics (26 papers) and Click Chemistry and Applications (15 papers). Stefano Forli is often cited by papers focused on Computational Drug Discovery Methods (31 papers), Protein Structure and Dynamics (26 papers) and Click Chemistry and Applications (15 papers). Stefano Forli collaborates with scholars based in United States, Italy and Germany. Stefano Forli's co-authors include Diogo Santos‐Martins, Andreas F. Tillack, Jérôme Eberhardt, Arthur J. Olson, David S. Goodsell, Michel F. Sanner, Michael E. Pique, Ruth Huey, Benjamin F. Cravatt and Keriann M. Backus and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Stefano Forli

88 papers receiving 10.6k citations

Hit Papers

AutoDock Vina 1.2.0: New ... 2010 2026 2015 2020 2021 2016 2016 2010 2015 1000 2.0k 3.0k
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. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings
    2021 3.6k citations Jérôme Eberhardt, Diogo Santos‐Martins et al. Journal of Chemical Information and Modeling profile →
  2. Computational protein–ligand docking and virtual drug screening with the AutoDock suite
    2016 1.7k citations Stefano Forli, Michel F. Sanner et al. profile →
  3. Proteome-wide covalent ligand discovery in native biological systems
    2016 636 citations Stefano Forli, Arthur J. Olson et al. profile →
  4. Virtual screening with AutoDock: theory and practice
    2010 528 citations Stefano Forli, David S. Goodsell et al. profile →
  5. AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility
    2015 431 citations Stefano Forli, David S. Goodsell et al. profile →
  6. “Inverse Drug Discovery” Strategy To Identify Proteins That Are Targeted by Latent Electrophiles As Exemplified by Aryl Fluorosulfates
    2017 237 citations D.E. Mortenson, Lars Plate et al. Journal of the American Chemical Society profile →
  7. Accelerating A uto D ock 4 with GPUs and Gradient-Based Local Search
    2021 192 citations Diogo Santos‐Martins, Leonardo Solis-Vasquez et al. Journal of Chemical Theory and Computation profile →
  8. SuFEx-enabled, agnostic discovery of covalent inhibitors of human neutrophil elastase
    2019 157 citations Qinheng Zheng, Jordan L. Woehl et al. Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stefano Forli United States 37 6.1k 2.9k 2.6k 1.1k 963 96 10.7k
Weiliang Zhu China 52 5.2k 0.8× 2.6k 0.9× 2.2k 0.9× 844 0.8× 1.1k 1.1× 404 11.3k
Jeremy R. Greenwood United States 27 6.8k 1.1× 2.9k 1.0× 3.2k 1.3× 1.2k 1.1× 1.2k 1.3× 67 12.1k
Tyler Day United States 22 6.1k 1.0× 1.9k 0.6× 2.7k 1.0× 1.0k 0.9× 1.0k 1.1× 24 10.4k
Gabriele Cruciani Italy 55 5.0k 0.8× 2.4k 0.8× 3.6k 1.4× 1.1k 1.0× 1.0k 1.0× 223 10.5k
Jonathan B. Baell Australia 39 5.1k 0.8× 2.5k 0.8× 2.2k 0.9× 665 0.6× 929 1.0× 177 9.2k
David B. Ascher Australia 46 6.9k 1.1× 1.7k 0.6× 2.6k 1.0× 808 0.7× 710 0.7× 187 11.8k
Stefano Moro Italy 56 7.4k 1.2× 3.6k 1.3× 1.8k 0.7× 1.2k 1.1× 745 0.8× 354 12.7k
Bernd Kuhn Switzerland 43 6.9k 1.1× 3.5k 1.2× 2.6k 1.0× 1.3k 1.2× 748 0.8× 109 12.7k
Kaixian Chen China 58 7.5k 1.2× 2.9k 1.0× 3.5k 1.4× 932 0.9× 1.3k 1.3× 437 13.9k
Leah L. Frye United States 18 7.1k 1.2× 2.9k 1.0× 3.8k 1.5× 1.3k 1.2× 1.4k 1.5× 32 11.8k

Countries citing papers authored by Stefano Forli

Since Specialization
Citations

This map shows the geographic impact of Stefano Forli'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 Stefano Forli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefano Forli more than expected).

Fields of papers citing papers by Stefano Forli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stefano Forli. 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 Stefano Forli. The network helps show where Stefano Forli may publish in the future.

Co-authorship network of co-authors of Stefano Forli

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

All Works

20 of 20 papers shown
1.
2.
Solis-Vasquez, Leonardo, Andreas F. Tillack, Diogo Santos‐Martins, Andreas Koch, & Stefano Forli. (2025). Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels. 793–803.
3.
Liman, Emily R., et al.. (2025). Structure-guided discovery of Otopetrin 1 inhibitors reveals druggable binding sites at the intrasubunit interface. Nature Communications. 16(1). 9362–9362.
4.
Fernández‐Quintero, Monica L., et al.. (2025). AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold. Journal of Chemical Information and Modeling. 66(1). 152–166. 1 indexed citations
5.
Governa, Paolo, Marco Biagi, Fabrizio Manetti, & Stefano Forli. (2024). Consensus screening for a challenging target: the quest for P-glycoprotein inhibitors. RSC Medicinal Chemistry. 15(2). 720–732. 3 indexed citations
6.
Loeffler, Johannes R., Franz Waibl, Patrick K. Quoika, et al.. (2024). The Role of Force Fields and Water Models in Protein Folding and Unfolding Dynamics. Journal of Chemical Theory and Computation. 20(5). 2321–2333. 14 indexed citations
7.
Wozniak, Jacob M., Weichao Li, Paolo Governa, et al.. (2024). Enhanced mapping of small-molecule binding sites in cells. Nature Chemical Biology. 20(7). 823–834. 17 indexed citations
8.
Silvestri, Anthony P., Dillon T. Flood, Matthew Holcomb, et al.. (2023). Stretching Peptides to Generate Small Molecule β-Strand Mimics. ACS Central Science. 9(4). 648–656. 13 indexed citations
9.
Cheng, Yunfei, Gencheng Li, Christopher J. Smedley, et al.. (2022). Diversity oriented clicking delivers β-substituted alkenyl sulfonyl fluorides as covalent human neutrophil elastase inhibitors. Proceedings of the National Academy of Sciences. 119(37). e2208540119–e2208540119. 31 indexed citations
10.
Sanner, Michel F., et al.. (2021). Improving Docking Power for Short Peptides Using Random Forest. Journal of Chemical Information and Modeling. 61(6). 3074–3090. 15 indexed citations
11.
Santos‐Martins, Diogo, Leonardo Solis-Vasquez, Andreas F. Tillack, et al.. (2021). Accelerating A uto D ock 4 with GPUs and Gradient-Based Local Search. Journal of Chemical Theory and Computation. 17(2). 1060–1073. 192 indexed citations breakdown →
12.
Eberhardt, Jérôme, Diogo Santos‐Martins, Andreas F. Tillack, & Stefano Forli. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling. 61(8). 3891–3898. 3596 indexed citations breakdown →
13.
Goodsell, David S., Michel F. Sanner, Arthur J. Olson, & Stefano Forli. (2020). TheAutoDocksuite at 30. Protein Science. 30(1). 31–43. 141 indexed citations
14.
Maeda, Shintaro, Hayashi Yamamoto, Lisa N. Kinch, et al.. (2020). Structure, lipid scrambling activity and role in autophagosome formation of ATG9A. Nature Structural & Molecular Biology. 27(12). 1194–1201. 213 indexed citations
15.
Ohtawa, Masaki, Shuming Chen, Sophia Khom, et al.. (2020). Synthetic, Mechanistic, and Biological Interrogation of Ginkgo biloba Chemical Space En Route to (−)-Bilobalide. Journal of the American Chemical Society. 142(43). 18599–18618. 44 indexed citations
16.
Zheng, Qinheng, Jordan L. Woehl, Seiya Kitamura, et al.. (2019). SuFEx-enabled, agnostic discovery of covalent inhibitors of human neutrophil elastase. Proceedings of the National Academy of Sciences. 116(38). 18808–18814. 157 indexed citations breakdown →
17.
Mosure, Sarah A., Jinsai Shang, Jérôme Eberhardt, et al.. (2019). Structural Basis of Altered Potency and Efficacy Displayed by a Major in Vivo Metabolite of the Antidiabetic PPARγ Drug Pioglitazone. Journal of Medicinal Chemistry. 62(4). 2008–2023. 30 indexed citations
18.
Zhang, Yuqi, et al.. (2019). AutoGridFR: Improvements on AutoDock Affinity Maps and Associated Software Tools. Journal of Computational Chemistry. 40(32). 2882–2886. 54 indexed citations
19.
Santos‐Martins, Diogo, Jérôme Eberhardt, Giulia Bianco, et al.. (2019). Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4. Journal of Computer-Aided Molecular Design. 33(12). 1011–1020. 53 indexed citations
20.
Mortenson, D.E., Lars Plate, Grant A. L. Bare, et al.. (2017). “Inverse Drug Discovery” Strategy To Identify Proteins That Are Targeted by Latent Electrophiles As Exemplified by Aryl Fluorosulfates. Journal of the American Chemical Society. 140(1). 200–210. 237 indexed citations breakdown →

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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