John D. Bickel

2.2k total citations · 1 hit paper
5 papers, 1.4k citations indexed

About

John D. Bickel is a scholar working on Molecular Biology, Computational Theory and Mathematics and Spectroscopy. According to data from OpenAlex, John D. Bickel has authored 5 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 2 papers in Spectroscopy. Recurrent topics in John D. Bickel's work include Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (2 papers) and Enzyme Structure and Function (2 papers). John D. Bickel is often cited by papers focused on Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (2 papers) and Enzyme Structure and Function (2 papers). John D. Bickel collaborates with scholars based in United States, Egypt and Germany. John D. Bickel's co-authors include Koushik Kasavajhala, Lauren Raguette, Kellon Belfon, He Huang, Chuan Tian, Angela N. Migues, Qin Wu, Yuzhang Wang, Carlos Simmerling and Robert C. Rizzo and has published in prestigious journals such as Journal of Computational Chemistry, Journal of Chemical Theory and Computation and Bioorganic Chemistry.

In The Last Decade

John D. Bickel

3 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution

2019 1.4k citations Chuan Tian, Koushik Kasavajhala 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						Papers	Cites
John D. Bickel United States	2	1.0k	225	217	121	114	5	1.4k
Angela N. Migues United States	5	997 1.0×	262 1.2×	208 1.0×	124 1.0×	115 1.0×	6	1.5k
Lauren Raguette United States	5	1.1k 1.1×	228 1.0×	243 1.1×	122 1.0×	115 1.0×	7	1.6k
Kellon Belfon United States	5	1.1k 1.1×	232 1.0×	242 1.1×	125 1.0×	116 1.0×	6	1.5k
David Sehnal Czechia	14	1.2k 1.2×	278 1.2×	166 0.8×	117 1.0×	120 1.1×	37	1.7k
Boris Aguilar United States	14	1.3k 1.3×	284 1.3×	291 1.3×	177 1.5×	86 0.8×	34	1.9k
Karim M. ElSawy United Kingdom	11	1.2k 1.2×	211 0.9×	236 1.1×	101 0.8×	76 0.7×	26	1.7k
Theresa A. Ramelot United States	21	1.4k 1.4×	371 1.6×	117 0.5×	116 1.0×	162 1.4×	69	1.9k
Robert Fraczkiewicz United States	10	828 0.8×	283 1.3×	160 0.7×	110 0.9×	141 1.2×	16	1.3k
Ruxi Qi China	16	783 0.8×	155 0.7×	211 1.0×	159 1.3×	62 0.5×	34	1.2k

Countries citing papers authored by John D. Bickel

Since Specialization

Citations

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

Fields of papers citing papers by John D. Bickel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John D. Bickel. 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 John D. Bickel. The network helps show where John D. Bickel may publish in the future.

Co-authorship network of co-authors of John D. Bickel

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

All Works

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5 of 5 papers shown

1.

Bickel, John D., et al.. (2024). Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK. Journal of Computational Chemistry. 46(1). e27508–e27508.

2.

Coant, Nicolas, John D. Bickel, Ronald J. Rahaim, et al.. (2023). Neutral ceramidase-active site inhibitor chemotypes and binding modes. Bioorganic Chemistry. 139. 106747–106747. 1 indexed citations

3.

Bickel, John D., et al.. (2023). Rapid Rescoring and Refinement of Ligand–Receptor Complexes Using Replica Exchange Molecular Dynamics with a Monte Carlo Pose Reservoir. Journal of Chemical Theory and Computation. 19(21). 7934–7945.

4.

Bickel, John D., et al.. (2022). A molecular evolution algorithm for ligand design in DOCK. Journal of Computational Chemistry. 43(29). 1942–1963. 11 indexed citations

5.

Tian, Chuan, Koushik Kasavajhala, Kellon Belfon, et al.. (2019). ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. Journal of Chemical Theory and Computation. 16(1). 528–552. 1385 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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