Karl Leswing

4.1k total citations · 3 hit papers
14 papers, 2.3k citations indexed

About

Karl Leswing is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Karl Leswing has authored 14 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Materials Chemistry, 11 papers in Computational Theory and Mathematics and 7 papers in Molecular Biology. Recurrent topics in Karl Leswing's work include Machine Learning in Materials Science (13 papers), Computational Drug Discovery Methods (11 papers) and Protein Structure and Dynamics (4 papers). Karl Leswing is often cited by papers focused on Machine Learning in Materials Science (13 papers), Computational Drug Discovery Methods (11 papers) and Protein Structure and Dynamics (4 papers). Karl Leswing collaborates with scholars based in United States and Japan. Karl Leswing's co-authors include Vijay S. Pande, Bharath Ramsundar, Caleb Geniesse, Zhenqin Wu, Joseph Gomes, Evan N. Feinberg, Robert Abel, Matthew P. Repasky, Steven V. Jerome and Kun Yao and has published in prestigious journals such as Chemistry of Materials, The Journal of Physical Chemistry B and Scientific Reports.

In The Last Decade

Karl Leswing

14 papers receiving 2.3k citations

Hit Papers

MoleculeNet: a benchmark for molecular machine learning 2017 2026 2020 2023 2017 2021 2023 500 1000 1.5k
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. MoleculeNet: a benchmark for molecular machine learning
    2017 1.7k citations Zhenqin Wu, Bharath Ramsundar et al. Chemical Science profile →
  2. Efficient Exploration of Chemical Space with Docking and Deep Learning
    2021 228 citations Yang Ying, Kun Yao et al. Journal of Chemical Theory and Computation profile →
  3. Epik: p K a and Protonation State Prediction through Machine Learning
    2023 130 citations Ryne C. Johnston, Kun Yao et al. Journal of Chemical Theory and Computation profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Karl Leswing United States 11 1.5k 1.3k 1.1k 304 132 14 2.3k
Joseph Gomes United States 10 1.5k 1.0× 1.6k 1.2× 1.2k 1.0× 376 1.2× 93 0.7× 26 2.6k
Thomas Blaschke Germany 11 1.9k 1.2× 1.3k 1.0× 1.4k 1.3× 227 0.7× 110 0.8× 13 2.6k
Evan N. Feinberg United States 9 1.7k 1.1× 1.3k 1.0× 2.0k 1.7× 306 1.0× 91 0.7× 17 3.2k
Steven Kearnes United States 8 1.1k 0.7× 1.2k 0.9× 723 0.6× 224 0.7× 72 0.5× 17 1.8k
Zhenqin Wu United States 9 1.6k 1.1× 1.3k 1.0× 1.3k 1.1× 339 1.1× 47 0.4× 16 2.4k
Esben Jannik Bjerrum Sweden 21 1.6k 1.0× 1.3k 1.0× 1.2k 1.0× 137 0.5× 95 0.7× 39 2.2k
Marcus Olivecrona Sweden 3 1.5k 1.0× 980 0.8× 1.1k 1.0× 203 0.7× 72 0.5× 3 2.1k
Marwin Segler United Kingdom 14 2.0k 1.3× 1.9k 1.5× 1.3k 1.2× 295 1.0× 223 1.7× 21 3.2k
Katja Hansen Germany 14 1.2k 0.8× 1.8k 1.4× 576 0.5× 162 0.5× 95 0.7× 16 2.4k
Christian Tyrchan Sweden 23 1.9k 1.3× 1.3k 1.0× 1.7k 1.5× 146 0.5× 247 1.9× 56 2.8k

Countries citing papers authored by Karl Leswing

Since Specialization
Citations

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

Fields of papers citing papers by Karl Leswing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Karl Leswing

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

All Works

14 of 14 papers shown
1.
Chew, Alex K., et al.. (2025). Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures. npj Computational Materials. 11(1). 2 indexed citations
2.
Matsuzawa, Nobuyuki, Keisuke Hayashi, Mohammad Atif Faiz Afzal, et al.. (2024). Exploring Molecules with Low Viscosity: Using Physics-Based Simulations and De Novo Design by Applying Reinforcement Learning. Chemistry of Materials. 36(23). 11706–11716. 1 indexed citations
3.
Johnston, Ryne C., Kun Yao, Karl Leswing, et al.. (2023). Epik: p K a and Protonation State Prediction through Machine Learning. Journal of Chemical Theory and Computation. 19(8). 2380–2388. 130 indexed citations breakdown →
4.
Browning, Andrea, et al.. (2023). Development of scalable and generalizable machine learned force field for polymers. Scientific Reports. 13(1). 17251–17251. 17 indexed citations
5.
Oliveira, César de, Karl Leswing, Shulu Feng, et al.. (2023). FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning. Journal of Chemical Information and Modeling. 63(17). 5592–5603. 7 indexed citations
6.
Jacobson, Leif D., James Stevenson, Farhad Ramezanghorbani, et al.. (2022). Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions. Journal of Chemical Theory and Computation. 18(4). 2354–2366. 42 indexed citations
7.
Agarwal, Garvit, James Stevenson, Leif D. Jacobson, et al.. (2022). High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations. The Journal of Physical Chemistry B. 126(33). 6271–6280. 52 indexed citations
8.
Leswing, Karl, Tim Robertson, Mathew D. Halls, et al.. (2022). De NovoDesign of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2. The Journal of Physical Chemistry A. 126(34). 5837–5852. 11 indexed citations
9.
Kwak, H. Shaun, David J. Giesen, Thomas F. Hughes, et al.. (2022). Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations. Frontiers in Chemistry. 9. 21 indexed citations
10.
Leswing, Karl, Tim Robertson, David J. Giesen, et al.. (2021). De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen. The Journal of Physical Chemistry A. 125(33). 7331–7343. 16 indexed citations
11.
Ying, Yang, Kun Yao, Matthew P. Repasky, et al.. (2021). Efficient Exploration of Chemical Space with Docking and Deep Learning. Journal of Chemical Theory and Computation. 17(11). 7106–7119. 228 indexed citations breakdown →
12.
Ghanakota, Phani, Pieter H. Bos, Kyle D. Konze, et al.. (2020). Combining Cloud-Based Free-Energy Calculations, Synthetically Aware Enumerations, and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization. Journal of Chemical Information and Modeling. 60(9). 4311–4325. 34 indexed citations
13.
Konze, Kyle D., Pieter H. Bos, Markus K. Dahlgren, et al.. (2019). Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors. Journal of Chemical Information and Modeling. 59(9). 3782–3793. 91 indexed citations
14.
Wu, Zhenqin, Bharath Ramsundar, Evan N. Feinberg, et al.. (2017). MoleculeNet: a benchmark for molecular machine learning. Chemical Science. 9(2). 513–530. 1696 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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Rankless by CCL
2026