scholarly journals Torsional potential energy surfaces and vibrational levels in trans Stilbene

2002 ◽  
Vol 612 (2-3) ◽  
pp. 383-391 ◽  
Author(s):  
Giorgio Orlandi ◽  
Laura Gagliardi ◽  
Sonia Melandri ◽  
Walther Caminati
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Torsional Potential ◽  
Trans Stilbene ◽  
Vibrational Levels ◽  
In Trans ◽  
Energy Surfaces

scholarly journals Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments

2020 ◽  
Author(s):  
Shae-Lynn Lahey ◽  
Từ Nguyễn Thiên Phúc ◽  
Christopher Rowley
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Force Field ◽  
Potential Energy Surfaces ◽  
Md Simulations ◽  
Absolute Deviation ◽  
Torsional Potential ◽  
The Mean ◽  
High Level ◽  
Energy Surfaces

Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly due to steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein--ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this paper, we compare four force fields (Generalized AMBER Force Field (GAFF), Open Force Field (OpenFF), CHARMM General Force Field (CGenFF), Optimized Potentials for Liquid Simulations (OPLS)) and two neural network potentials (ANI-2x, ANI-1ccx) in their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The mean of the absolute deviation over the full PES (MADF) and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data was used as a measure of accuracy. In comparison to high-level ab-initio data, ANI-1ccx was most accurate for predicting the barrier height (MADF: 0.5~kcal/mol, MADB:~0.8~kcal/mol), followed closely by ANI-2x (MADF: 0.5~kcal/mol, MADB:~1.0~kcal/mol), then CGenFF (MADF: 0.8~kcal/mol, MADB: 1.3~kcal/mol), OpenFF (MADF: 1.5~kcal/mol, MADB: 1.4~kcal/mol), GAFF (MADF: 1.2~kcal/mol, MADB: 2.6~kcal/mol), and finally OPLS (MADF: 1.5~kcal/mol, MADB: 2.8~kcal/mol). Significantly, the NNPs are systematically more accurate and more reliable than any of the force fields. As a practical example, the neural network potential/molecular mechanics (NNP/MM) method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multi-nanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force enhanced sampling techniques. These theories predicted a rate of isomerization of $4.30 \times 10^{-1}$ ns$^{-1}$, which is consistent with direct MD simulations.

Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments

2020 ◽  
Author(s):  
Shae-Lynn Lahey ◽  
Từ Nguyễn Thiên Phúc ◽  
Christopher Rowley
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Force Field ◽  
Potential Energy Surfaces ◽  
Md Simulations ◽  
Absolute Deviation ◽  
Torsional Potential ◽  
The Mean ◽  
High Level ◽  
Energy Surfaces

Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly due to steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein--ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this paper, we compare four force fields (Generalized AMBER Force Field (GAFF), Open Force Field (OpenFF), CHARMM General Force Field (CGenFF), Optimized Potentials for Liquid Simulations (OPLS)) and two neural network potentials (ANI-2x, ANI-1ccx) in their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The mean of the absolute deviation over the full PES (MADF) and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data was used as a measure of accuracy. In comparison to high-level ab-initio data, ANI-1ccx was most accurate for predicting the barrier height (MADF: 0.5~kcal/mol, MADB:~0.8~kcal/mol), followed closely by ANI-2x (MADF: 0.5~kcal/mol, MADB:~1.0~kcal/mol), then CGenFF (MADF: 0.8~kcal/mol, MADB: 1.3~kcal/mol), OpenFF (MADF: 1.5~kcal/mol, MADB: 1.4~kcal/mol), GAFF (MADF: 1.2~kcal/mol, MADB: 2.6~kcal/mol), and finally OPLS (MADF: 1.5~kcal/mol, MADB: 2.8~kcal/mol). Significantly, the NNPs are systematically more accurate and more reliable than any of the force fields. As a practical example, the neural network potential/molecular mechanics (NNP/MM) method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multi-nanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force enhanced sampling techniques. These theories predicted a rate of isomerization of $4.30 \times 10^{-1}$ ns$^{-1}$, which is consistent with direct MD simulations.

scholarly journals Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments

2020 ◽  
Author(s):  
Shae-Lynn Lahey ◽  
Từ Nguyễn Thiên Phúc ◽  
Christopher Rowley
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Force Field ◽  
Potential Energy Surfaces ◽  
Md Simulations ◽  
Absolute Deviation ◽  
Torsional Potential ◽  
The Mean ◽  
High Level ◽  
Energy Surfaces

Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly due to steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein--ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this paper, we compare four force fields (Generalized AMBER Force Field (GAFF), Open Force Field (OpenFF), CHARMM General Force Field (CGenFF), Optimized Potentials for Liquid Simulations (OPLS)) and two neural network potentials (ANI-2x, ANI-1ccx) in their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The mean of the absolute deviation over the full PES (MADF) and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data was used as a measure of accuracy. In comparison to high-level ab-initio data, ANI-1ccx was most accurate for predicting the barrier height (MADF: 0.5~kcal/mol, MADB:~0.8~kcal/mol), followed closely by ANI-2x (MADF: 0.5~kcal/mol, MADB:~1.0~kcal/mol), then CGenFF (MADF: 0.8~kcal/mol, MADB: 1.3~kcal/mol), OpenFF (MADF: 1.5~kcal/mol, MADB: 1.4~kcal/mol), GAFF (MADF: 1.2~kcal/mol, MADB: 2.6~kcal/mol), and finally OPLS (MADF: 1.5~kcal/mol, MADB: 2.8~kcal/mol). Significantly, the NNPs are systematically more accurate and more reliable than any of the force fields. As a practical example, the neural network potential/molecular mechanics (NNP/MM) method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multi-nanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force enhanced sampling techniques. These theories predicted a rate of isomerization of $4.30 \times 10^{-1}$ ns$^{-1}$, which is consistent with direct MD simulations.

scholarly journals Torsional Potential Energy Surfaces of Dinitrobenzene Isomers

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Paul M. Smith ◽  
Mario F. Borunda
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Three Dimensional ◽  
Molecular Structures ◽  
Basis Sets ◽  
Valuable Insight ◽  
Complex Molecules ◽  
Torsional Potential ◽  
Energy Surfaces

The torsional potential energy surfaces of 1,2-dinitrobenzene, 1,3-dinitrobenzene, and 1,4-dinitrobenzene were calculated using the B3LYP functional with 6-31G(d) basis sets. Three-dimensional energy surfaces were created, allowing each of the two C-N bonds to rotate through 64 positions. Dinitrobenzene was chosen for the study because each of the three different isomers has widely varying steric hindrances and bond hybridization, which affect the energy of each conformation of the isomers as the nitro functional groups rotate. The accuracy of the method is determined by comparison with previous theoretical and experimental results. The surfaces provide valuable insight into the mechanics of conjugated molecules. The computation of potential energy surfaces has powerful application in modeling molecular structures, making the determination of the lowest energy conformations of complex molecules far more computationally accessible.

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