CHARMM: The Energy Function and Its Parameterization

By Alexander D. MacKerell, Bernard R. Brooks, C. Brooks, L. Nilsson, Benoît Roux, Youngdo Won, Martin Karplus. 2002

405 citations recorded by the source corpus.

Subfields: Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/18bfd715f119dc740f75388c7c97334b327d26ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Ivan V. Anishchenko
2. David Baker
3. Walter Thiel
4. Hans Senn
5. Partha P. Talukdar
6. Soumya Sanyal
7. Yang Zhang
8. Tanja Kortemme

Citations in the corpus

• ProteinGCN: Protein model quality assessment using Graph Convolutional Networks (2020, methodology): “Various methods have been developed to tackle the scoring problem, ranging from energy functions derived from either general physical principles (like a number of popular molecular mechanics force fields CHARMM [18], AMBER [6], OPLS [13], GROMOS 3.2) and uses GCN to learn the atom embeddings.”
• QM/MM Methods for Biological Systems (2006, methodology): “• MM packages with QM: amber [218, 702], boss (MC simulations, semiempirical QM only) [219, 703], charmm [180, 704] • QM packages with MM: adf [220, 705], gamess-uk [221, 706], Gaussian [707], NWChem [222, 708], QSite/Jaguar [709]; Car–Parrinello MD codes with QM/MM capabilities: cpmd [101, 119, 131”