TIES_MD can be installed with the Conda package manager. Assuming the user does not have Conda the steps to do this are as follows, starting with an install of Miniconda:

chmod +x
./ -b -p $prefix
export PATH="$prefix/bin:$PATH"

Ensure the Miniconda used matches the platform on which you are running, for example use for Linux-ppc64le machines, and that $prefix is set to some directory with read write permissions.


If you are attempting a Linux-ppc64le read the final section of this page.

With Conda installed TIES MD can be installed with with:

conda install -c conda-forge ties_md

The install of OpenMM which was installed with TIES_MD can be verified by running:

python -m openmm.testInstallation

for older OpenMM versions < 7.6 this command was:

python -m simtk.testInstallation

In some instances the wrong version of OpenMM and CUDAtoolkit could be installed. If this has happened the above test of OpenMM will produce an output which looks like:

OpenMM Version: 7.7
Git Revision: 130124a3f9277b054ec40927360a6ad20c8f5fa6

There are 4 Platforms available:

1 Reference - Successfully computed forces
2 CPU - Successfully computed forces
3 CUDA - Error computing forces with CUDA platform
4 OpenCL - Successfully computed forces

CUDA platform error: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)

Median difference in forces between platforms:

Reference vs. CPU: 6.30571e-06
Reference vs. OpenCL: 6.76359e-06
CPU vs. OpenCL: 8.05194e-07

All differences are within tolerance.

the critical information here is 3 CUDA - Error computing forces with CUDA platform and CUDA platform error: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222) this can be corrected by changing the install version of CUDAtoolkit like so:

conda install -c conda-forge openmm cudatoolkit=10.0

where 10.0 should be replaced with the particular CUDA version you want to target. One can determine an appropriate value for this by running nvidia-smi in a terminal which yields:

| NVIDIA-SMI 460.80       Driver Version: 460.80       CUDA Version: 11.2     |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|   0  Quadro M1000M       Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   50C    P5    N/A /  N/A |    435MiB /  2002MiB |      4%      Default |
|                               |                      |                  N/A |

The top right value here 11.2 can be used as the version of CUDA you wish to target. If nvidia-smi does not return the above output your GPU and or drivers my not be configured correctly.

The install of TIES MD can be tested by downloading and running (Tutorial) any of the examples provided here. These examples can be download by running:

git clone

TIES OpenMM linux-ppc64le


There is no version of PyMABR 4.0.1 for linux-ppc64le therefore FEP analysis will not work until this is updated. To work around this FEP simulations can be run on linux-ppc64le but the result must be copied elsewhere for analysis.

To use TIES_MD on linux-ppc64le skip the above install of TIES MD and instead run:

conda install -c ucl-ccs ties_md

Then install OpenMM with:

conda install -c conda-forge openmm

And to use the OpenMM protocol a custom version of OpenMMTools is also needed to perform the alchemical transformations of the system and allow for thermodynamic integration calculations. In order to install the custom version of OpenMMTools run:

mkdir openmmtools_install
cd openmmtools_install
git clone -b adw62-PowerPC
pip install ./openmmtools --use-feature=in-tree-build