Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility

17 December 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat myriad diseases. In this work we examine the computation of alchemical relative binding free energies with an eye to assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2 and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2 and NAMD3. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between packages of 0.5 $kcal/mol$ The correlation between packages is very good with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficient between two packages being 0.91, 0.89 and 0.74 respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.


Free energy
Molecular Dynamics

Supplementary materials

Supplementary Information: Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility
Individual results for relative binding free energy calculations performed as well as the input used to generate these results https: // The results provided in the SI come from the main TIES protocol (table S1-S6) and the long and large ensemble TIES protocols table S7. Additionally we present an equivalent version of figure 10 plotted using decorrelated data (figure S1). We provide information on the long time simulations performed in this work with NAMD3 and highlight some statistically significant results we observe in figures S2-S3. These figures are complemented by an assessment of the accuracy of NAMD3 calculations across different simulation protocols in Table S8. Table S9 provides the PDB codes for the proteins used as input to this work. Table S10 presents more detailed information for the performance of MD engines at different system sizes. Finally, we provide a full list of settings used in the different MD engines.

Supplementary weblinks


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