scholarly journals Assessment of GAFF2 and OPLS-AA General Force Fields in Combination with the Water Models TIP3P, SPCE, and OPC3 for the Solvation Free Energy of Druglike Organic Molecules

2019 ◽  
Vol 15 (3) ◽  
pp. 1983-1995 ◽  
Author(s):  
Dario Vassetti ◽  
Marco Pagliai ◽  
Piero Procacci
Keyword(s):  
Free Energy ◽  
Organic Molecules ◽  
Force Fields ◽  
Solvation Free Energy ◽  
Water Models ◽  
General Force

scholarly journals Effects of Water Models on Binding Affinity: Evidence from All-Atom Simulation of Binding of Tamiflu to A/H5N1 Neuraminidase

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Trang Truc Nguyen ◽  
Man Hoang Viet ◽  
Mai Suan Li
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Force Fields ◽  
Binding Free Energy ◽  
Molecular Mechanic ◽  
Water Density ◽  
Wild Type ◽  
Oseltamivir Carboxylate ◽  
Water Models

The influence of water models SPC, SPC/E, TIP3P, and TIP4P on ligand binding affinity is examined by calculating the binding free energyΔGbindof oseltamivir carboxylate (Tamiflu) to the wild type of glycoprotein neuraminidase from the pandemic A/H5N1 virus.ΔGbindis estimated by the Molecular Mechanic-Poisson Boltzmann Surface Area method and all-atom simulations with different combinations of these aqueous models and four force fields AMBER99SB, CHARMM27, GROMOS96 43a1, and OPLS-AA/L. It is shown that there is no correlation between the binding free energy and the water density in the binding pocket in CHARMM. However, for three remaining force fieldsΔGbinddecays with increase of water density. SPC/E provides the lowest binding free energy for any force field, while the water effect is the most pronounced in CHARMM. In agreement with the popular GROMACS recommendation, the binding score obtained by combinations of AMBER-TIP3P, OPLS-TIP4P, and GROMOS-SPC is the most relevant to the experiments. For wild-type neuraminidase we have found that SPC is more suitable for CHARMM than TIP3P recommended by GROMACS for studying ligand binding. However, our study for three of its mutants reveals that TIP3P is presumably the best choice for CHARMM.

scholarly journals Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

2021 ◽  
Author(s):  
Peiyuan Gao ◽  
Xiu Yang ◽  
Yuhang Tang ◽  
Muqing Zheng ◽  
Amity Andersen ◽  
...  
Keyword(s):  
Free Energy ◽  
Organic Molecules ◽  
Equilibrium Constants ◽  
Gaussian Process Regression ◽  
Solvation Free Energy ◽  
Emergent Properties ◽  
Redox Potentials ◽  
Energy Prediction ◽  
Aqueous Solvation ◽  
Graphical Gaussian

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox...

“Bimolecular” QSPR: Estimation of the solvation free energy of organic molecules in different solvents

2007 ◽  
Vol 414 (1) ◽  
pp. 128-131 ◽  
Author(s):  
A. A. Kravtsov ◽  
P. V. Karpov ◽  
I. I. Baskin ◽  
V. A. Palyulin ◽  
N. S. Zefirov
Keyword(s):  
Free Energy ◽  
Organic Molecules ◽  
Solvation Free Energy

Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

2018 ◽  
Author(s):  
Maximiliano Riquelme ◽  
Alejandro Lara ◽  
David L. Mobley ◽  
Toon Vestraelen ◽  
Adelio R Matamala ◽  
...  
Keyword(s):  
Force Field ◽  
Functional Groups ◽  
Electrostatic Interactions ◽  
Organic Molecules ◽  
Force Fields ◽  
Chemical Elements ◽  
Atomic Charges ◽  
Free Energies ◽  
Molecular Systems ◽  
Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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