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If you experience any issues during any stage of the procedure, please raise issues here. You can also receive support on the Open Force Field Slack channel benchmarks-partnerssupport
NOTE: all discussions are public, so please do not disclose any molecule information you wish to remain private.
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Installation using conda
conda
environments have been created which are fully self-consistent and contain at a minimum all of the required dependencies to run the calculations and generate the final analysis.
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Any update to the workflow will be distributed via an update to this environment.
Installing openff-benchmark
for testing
While we are testing the package, you will need to clone and install the openff-benchmark
package from source:
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conda activate openff-benchmark-optimization
git clone https://github.com/openforcefield/openff-benchmark.git
cd openff-benchmark
pip install -e .
This will make the openff-benchmark
CLI available to you.
Software Updates
Whenever we need to update any of the software with bug fixes or improvements we will update the conda environment above. To install these updates it is recommended that you remove the current working environment with conda env remove -n openff-benchmark-optimization
and install again using the instructions above.
Installation using a single-file installer
If your compute environment cannot reach Anaconda Cloud, you can install the conda environment via a single-file installer.
Navigate to the Releases page for
openff-benchmark
.For the most recent release in the list, click on the Assets dropdown, and download the
ubuntu-latest_py3.7.sh.zip
file. This is ~1GB, so the download may take a moment.Upload the file to your compute environment/cluster.
Unzip with
unzip ubuntu-latest_py3.7.sh.zip
.Run the installer with:
bash openff-benchmark-*/openff-benchmark-*.sh -b -p openff-benchmark-miniconda
, substituting the name of the unzipped directory and the install script.Add
conda
executable to your environment withsource openff-benchmark-miniconda/etc/profile.d/conda.sh
Activate the
base
environment withconda activate
. This will be the name of your environment instead ofopenff-benchmark-optimization
, which you will see used elsewhere.
To make it easy to load this environment at will, we recommend you create an openff-benchmark-optimization.sh
file with the following contents:
Code Block |
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source openff-benchmark-miniconda/etc/profile.d/conda.sh
conda activate |
Software Updates
If you used the single-file installer approach, you will need to remove or rename the openff-benchmark-miniconda
directory created from installation and repeat the install instructions above.
Compute Approaches
Multiple compute options have been created to allow users to generate the QM and MM optimizations required for the benchmark. The main difference between these is the use of a long-running self-hosted QCFractal server instance to store and collect all data during the benchmark.
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Where is the local QCArchive database stored
you can find the database in a path like
~/.qca/qcfractal/
on the machine where you activated the server.
Why do I see stereochemistry issues in the initial step in the workflow?
All file reading and writing is done via the Open Force Field Toolkit, with many cheminformatics operations powered by RDKit. When we read in a molecule we check with RDKit if the molecule has any missing stereochemistry information or for other common formatting issues. We recommend using 3D SDF files where possible so that all stereochemistry is fully defined.
Missing molecules?
During In each stage of the workflow, things can go wrong involving undefined stereochemistry when a small fraction of molecules will encounter issues with the various programs in the pipeline. When this happens a warning is shown about , and the molecule is moved to an error directory to keep track ofsubdirectory of the output folder.
During the analysis, you may notice some molecules are missing or the number of conformers of the molecule is not as expected. During optimization, we sometimes see proton transfer and the resulting optimized geometry is actually a tautomer of the input. In this case, the optimization is excluded from the analysis. This should not affect the results.
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