All of our software is open-source and freely available on GitHub! Please let us know if you find any of our software useful and please cite the relevant publications if you use it in your research. Contact us on the GitHub Issues of each software to report bugs or discuss the integration of our software libraries into your research. 

AToM-OpenMM is a middleware to run absolute and relative alchemical binding free energy calculations with OpenMM. It is based on our Alchemical Transfer Method (ATM) and a Parallel Asynchronous Hamiltonian Replica Exchange conformational sampling algorithm.

AToM-OpenMM targets difficult ligand transformations involving scaffold-hopping and charge-changing transformations that are not well supported by traditional algorithms. Head over to the AToM-OpenMM Tutorials to start computing binding free energies.   

AToM-OpenMM is written in Python and is easily installed from the Github repository together with the OpenMM ATM Meta Force Plugin from conda-forge.

Asynchronous replica exchange (or ASyncRE for short) is an implementation of the popular parallel replica-exchange conformational sampling algorithm. Unlike traditional synchronous implementations, it can run on heterogeneous hardware (GPUs and CPUs of various speeds), survives hardware failures, and supports preemption.

This work is supported by a generous grant from the National Science Foundation.

Our Alchemical Transfer Method (ATM) for absolute and relative binding free energies is implemented as an  OpenMM plugin called the ATM Meta-Force Plugin.

ATM is an alchemical approach to binding free energy estimation based on a displacement of the ligand between the binding site and the solvent bulk. At each time step, the potential energy and the forces of the solvated complex are evaluated with the ligand in the binding site and again after the ligand is displaced into the solvent bulk. The energies and forces are then hybridized according to an alchemical bridging potential to propagate the system in time. Relative binding free energies are supported in the same way by a coupled displacement of the two ligands in opposite directions. The ATM Meta-Force Plugin implements the ATM alchemical potential as an OpenMM Force object. In this way, any conformational sampling algorithm and force field available in OpenMM can be used in conjunction with ATM.

This work is supported by a generous grant from the National Science Foundation.


AGBNP (see AGBNP and AGBNP2 publications) is an analytic implicit hydration model primarily aimed at alchemical binding free energy calculations. The legacy CPU version of AGBNP is freely available as a portable library written in C. More recently, we have been focusing on a GPU implementation of AGBNP as part of the OpenMM library which is many times faster than the best CPU implementation. The AGBNP OpenMM Plugin is freely available on Github under the GPL license. The plugin currently implements on GPUs AGBNP version 1 (as well as the GaussVol model, see below).

GaussVol OpenMM Plugin

The GaussVol plugin implements in OpenMM the Gaussian model of Grants & Pickup for the fast calculation of the volume and surface area of macromolecules. The model is the basis of the AGBNP model above. The plugin enables molecular dynamics simulations on GPUs with an effective potential energy function proportional to the solute volume and/or the solute surface area as in, for instance, models of hydrophobic solvation. The GPU implementation is described in a publication in the Journal of Computational Chemistry. The original GaussVol plugin code is available on Github. Development and support of GaussVol continue as part of the AGBNP OpenMM Plugin described above.

overlap tree

Unbinned-WHAM R Package

UWHAM is an R package for multi-state free energy estimation and thermodynamic reweighting I developed jointly with Zhiqiang Tan at the Department of Statistics at Rutgers University. It provides to R users functionality similar to the MBAR python package. MBAR and UWHAM return identical point estimates of free energies and expectations using different numerical routes. See the UWHAM publication.To install UWHAM enter:

> install.packages("UWHAM") 

within an R session. Documentation and examples are available within R by entering

> library("UWHAM") > help(uwham) 

or in pdf format from

Variance estimation is based on the Fisher or Sandwich formulas, or, for correlated data, by block bootstrap. Processes corresponding to multiple independent simulations, serial tempering or serial Hamiltonian hopping simulations, and parallel replica exchange simulations of both the synchronous and asynchronous varieties are supported.

The UWHAM package is particularly robust in terms of scalability; we have been able to process within minutes large 2D replica-exchange datasets with ~300,000 snapshots from ~200 thermodynamic states.