z-logo
open-access-imgOpen Access
Uncertainty Quantification in Alchemical Free Energy Methods
Author(s) -
Agastya P. Bhati,
Shunzhou Wan,
Yuan Hu,
Brad Sherborne,
Peter V. Coveney
Publication year - 2018
Publication title -
journal of chemical theory and computation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.001
H-Index - 185
eISSN - 1549-9626
pISSN - 1549-9618
DOI - 10.1021/acs.jctc.7b01143
Subject(s) - computer science , estimator , energy (signal processing) , molecular dynamics , measure (data warehouse) , sampling (signal processing) , free energy perturbation , energy landscape , statistical physics , uncertainty quantification , data mining , statistics , machine learning , chemistry , mathematics , physics , computational chemistry , thermodynamics , filter (signal processing) , computer vision
Alchemical free energy methods have gained much importance recently from several reports of improved ligand-protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation-an ensemble of independent MD simulations-which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom