Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field
Author:
Affiliation:
1. School of Pharmacy, University of Maryland, Baltimore, 20 Penn Street, HSFII, Baltimore, Maryland 21201, United States
Funder
National Institute of General Medical Sciences
Publisher
American Chemical Society (ACS)
Subject
Physical and Theoretical Chemistry,Computer Science Applications
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.1c01166
Reference63 articles.
1. Development of CHARMM Polarizable Force Field for Nucleic Acid Bases Based on the Classical Drude Oscillator Model
2. Assessment of standard force field models against high-qualityab initiopotential curves for prototypes of π-π, CH/π, and SH/π interactions
3. Dynamical Fluctuating Charge Force Fields: The Aqueous Solvation of Amides
4. Dynamical fluctuating charge force fields: Application to liquid water
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