Unknown

Dataset Information

0

An efficient protocol for obtaining accurate hydration free energies using quantum chemistry and reweighting from molecular dynamics simulations.


ABSTRACT: The non-Boltzmann Bennett (NBB) free energy estimator method is applied to 21 molecules from the blind subset of the SAMPL4 challenge. When NBB is applied with the SMD implicit solvent model, and the OLYP/DZP level of quantum chemistry, highly accurate hydration free energy calculations are obtained with respect to experiment (RMSD=0.89kcal·mol-1). Other quantum chemical methods are also tested, and the effects of solvent model, density functional, basis set are explored in this benchmarking study, providing a framework for improvements in calculating hydration free energies. We provide a practical guide for using the best QM-NBB protocols that are consistently more accurate than either pure QM or pure MM alone. In situations where high accuracy hydration free energy predictions are needed, the QM-NBB method with SMD implicit solvent should be the first choice of quantum chemists.

SUBMITTER: Pickard FC 

PROVIDER: S-EPMC5068830 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

An efficient protocol for obtaining accurate hydration free energies using quantum chemistry and reweighting from molecular dynamics simulations.

Pickard Frank C FC   König Gerhard G   Simmonett Andrew C AC   Shao Yihan Y   Brooks Bernard R BR  

Bioorganic & medicinal chemistry 20160822 20


The non-Boltzmann Bennett (NBB) free energy estimator method is applied to 21 molecules from the blind subset of the SAMPL4 challenge. When NBB is applied with the SMD implicit solvent model, and the OLYP/DZP level of quantum chemistry, highly accurate hydration free energy calculations are obtained with respect to experiment (RMSD=0.89kcal·mol<sup>-1</sup>). Other quantum chemical methods are also tested, and the effects of solvent model, density functional, basis set are explored in this bench  ...[more]

Similar Datasets

| S-EPMC2684682 | biostudies-literature
| S-EPMC4003403 | biostudies-literature
| S-EPMC3583515 | biostudies-literature
| S-EPMC7125455 | biostudies-literature
| S-EPMC6713865 | biostudies-literature