Force Field Parametrization of Metal Ions from Statistical Learning Techniques-Reference-Cited by-同舟云学术

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Force Field Parametrization of Metal Ions from Statistical Learning Techniques

Published:2017-12-06 Issue:1 Volume:14 Page:255-273
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Fracchia Francesco¹^ORCID,Del Frate Gianluca¹,Mancini Giordano¹²,Rocchia Walter³^ORCID,Barone Vincenzo¹²

Affiliation:

1. Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy

2. Istituto Nazionale di Fisica Nucleare (INFN) sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy

3. Department of Drug Discovery and Development, Istituto Italiano di Tecnologia, 16163 Genova, Italy

Funder

FP7 Ideas: European Research Council

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.jctc.7b00779

Reference93 articles.

1. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations

2. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

3. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields

4. Definition and testing of the GROMOS force-field versions 54A7 and 54B7

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