A Smart Monte Carlo Technique for Free Energy Simulations of Multiconformational Molecules. Direct Calculations of the Conformational Populations of Organic Molecules

1995 ◽  
Vol 117 (31) ◽  
pp. 8211-8219 ◽  
Author(s):  
Hanoch Senderowitz ◽  
Frank Guarnieri ◽  
W. Clark Still
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Organic Molecules ◽  
Monte Carlo Technique ◽  
Energy Simulations ◽  
Direct Calculations

Prediction of Alkanolamine pKa Values by Combined Molecular Dynamics Free Energy Simulations and ab initio Calculations

2019 ◽  
Author(s):  
Javad Noroozi ◽  
William Smith
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio Calculations ◽  
Gas Phase ◽  
Quantum Chemical ◽  
Phase Reaction ◽  
Pka Values ◽  
Gas Phase Reaction ◽  
Reaction Free Energy ◽  
Energy Simulations

We use molecular dynamics free energy simulations in conjunction with quantum chemical calculations of gas phase reaction free energy to predict alkanolamines pka values. <br>

Application of the Monte Carlo Method for the Prediction of Behavior of Peptides

2019 ◽  
Vol 20 (12) ◽  
pp. 1151-1157 ◽  
Author(s):  
Alla P. Toropova ◽  
Andrey A. Toropov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Natural Sciences ◽  
Monte Carlo Technique ◽  
Quantitative Structure ◽  
Structure Property ◽  
The Monte Carlo Method ◽  
Modern Natural

Prediction of physicochemical and biochemical behavior of peptides is an important and attractive task of the modern natural sciences, since these substances have a key role in life processes. The Monte Carlo technique is a possible way to solve the above task. The Monte Carlo method is a tool with different applications relative to the study of peptides: (i) analysis of the 3D configurations (conformers); (ii) establishment of quantitative structure – property / activity relationships (QSPRs/QSARs); and (iii) development of databases on the biopolymers. Current ideas related to application of the Monte Carlo technique for studying peptides and biopolymers have been discussed in this review.

Examining sharp restart in a Monte Carlo method for the linearized Poisson–Boltzmann equation

2020 ◽  
Vol 26 (3) ◽  
pp. 223-244
Author(s):  
W. John Thrasher ◽  
Michael Mascagni
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Monte Carlo Algorithm ◽  
Significant Bias ◽  
Poisson Boltzmann ◽  
Electrostatic Free Energy ◽  
Poisson Boltzmann Equation ◽  
The Stability ◽  
Potential Methods ◽  
The Individual

AbstractIt has been shown that when using a Monte Carlo algorithm to estimate the electrostatic free energy of a biomolecule in a solution, individual random walks can become entrapped in the geometry. We examine a proposed solution, using a sharp restart during the Walk-on-Subdomains step, in more detail. We show that the point at which this solution introduces significant bias is related to properties intrinsic to the molecule being examined. We also examine two potential methods of generating a sharp restart point and show that they both cause no significant bias in the examined molecules and increase the stability of the run times of the individual walks.

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