Alexander D. MacKerell

Molecular Biology · 1998–2017

A researcher in the Science corpus: 28 papers; trusts 43 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 165 citations into it; #407 of 17,773 by trust here, #765 by citations
• Atomic and Molecular Physics, and Optics — 88 citations into it; #468 of 7,135 by trust here, #454 by citations
• Materials Chemistry — 82 citations into it; #1,039 of 11,513 by trust here, #2,552 by citations
• Spectroscopy — 36 citations into it; #352 of 1,674 by trust here, #355 by citations
• Computational Theory and Mathematics — 31 citations into it; #8,546 of 12,182 by trust here, #2,419 by citations
• Physical and Theoretical Chemistry — 30 citations into it; #232 of 671 by trust here, #65 by citations
• Computational Chemistry & Drug Discovery — 29 citations into it
• Organic Chemistry — 22 citations into it

Whom Alexander D. MacKerell trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. W. L. Jorgensen
2. Kenno Vanommeslaeghe
3. Bernard R. Brooks
4. Junmei Wang
5. David A. Case
6. T. Darden
7. Michael Feig
8. Michael R. Shirts
9. Charles L. Brooks III
10. Nicolas Foloppe

Who trusts Alexander D. MacKerell overall

1. Michael Feig
2. Justin A. Lemkul
3. Markus J. Buehler
4. Wenbo Yu
5. Sunhwan Jo
6. Lim Heo
7. Hans Senn
8. F. Boas
9. Sandeep Patel
10. James C. Gumbart

Papers from Alexander D. MacKerell's perspective

Ordered by how strongly the researchers Alexander D. MacKerell trusts endorse each paper. Same corpus, a different order for every researcher.

1. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters (International Conference on Software Composition)
2. A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules J. Am. Chem. Soc. 1995, 117, 5179−5197
3. VMD: visual molecular dynamics.
4. The Protein Data Bank: a computer-based archival file for macromolecular structures.
5. Automation of the CHARMM General Force Field (CGenFF) I: Bond Perception and Atom Typing (Journal of Chemical Information and Modeling)
6. CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields
7. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading
8. Generalized born models of macromolecular solvation effects. (Annual review of physical chemistry (Print))
9. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
10. The Protein Data Bank.

Their papers

• Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations (2004) — 3,302 citations
• Empirical force fields for biological macromolecules: Overview and issues (2004) — 1,206 citations
• CHARMM: The Energy Function and Its Parameterization (2002) — 405 citations
• Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles (2012) — 4,191 citations
• CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields (2009) — 6,664 citations
• Automation of the CHARMM General Force Field (CGenFF) I: Bond Perception and Atom Typing (2012) — 1,912 citations
• Automation of the CHARMM General Force Field (CGenFF) II: Assignment of bonded parameters and partial atomic charges (2012) — 1,579 citations
• Development and current status of the CHARMM force field for nucleic acids (2000) — 975 citations
• Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. (2011) — 257 citations
• CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field (2015) — 3,937 citations
• All‐atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data (2000) — 934 citations
• Improved treatment of the protein backbone in empirical force fields. (2004) — 929 citations
• Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types (2010) — 4,218 citations
• Extension of the CHARMM General Force Field to Sulfonyl-Containing Compounds and Its Utility in Biomolecular Simulations (2012) — 825 citations
• Computational Biochemistry and Biophysics (2001) — 361 citations
• CHARMM Additive All-Atom Force Field for Glycosidic Linkages between Hexopyranoses (2009) — 632 citations
• CHARMM additive all-atom force field for carbohydrate derivatives and its utility in polysaccharide and carbohydrate-protein modeling (2011) — 645 citations
• Additive empirical force field for hexopyranose monosaccharides (2008) — 519 citations
• CHARMM fluctuating charge force field for proteins: II Protein/solvent properties from molecular dynamics simulations using a nonadditive electrostatic model (2004) — 407 citations
• CHARMM-GUI 10 Years for Biomolecular Modeling and Simulation (2016) — 315 citations