CHARMM36m: an improved force field for folded and intrinsically disordered proteins

4.5k indexed citations

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This paper, published in 2016, received 4.5k indexed citations. Written by Jing Huang, Sarah Rauscher, Grzegorz Nawrocki, Ting Ran, Michael Feig, Bert L. de Groot and Helmut Grubmüller covering the research area of Molecular Biology and Materials Chemistry. It is primarily cited by scholars working on Molecular Biology (3.3k citations), Materials Chemistry (631 citations) and Computational Theory and Mathematics (422 citations). Published in Nature Methods.

Countries where authors are citing CHARMM36m: an improved force field for folded and intrinsically disordered proteins

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This map shows the geographic impact of CHARMM36m: an improved force field for folded and intrinsically disordered proteins. 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 CHARMM36m: an improved force field for folded and intrinsically disordered proteins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites CHARMM36m: an improved force field for folded and intrinsically disordered proteins more than expected).

Fields of papers citing CHARMM36m: an improved force field for folded and intrinsically disordered proteins

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the CHARMM36m: an improved force field for folded and intrinsically disordered proteins.

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.

This paper is also available at doi.org/10.1038/nmeth.4067.

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