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Similarity based Molecular Generation (SiMGen)
SiMGen is a local similarity based molecular generation method. It uses a pretrained MACE model to generate local molecular descriptors and a time-dependent similarity kernel to generate new molecules.
SiMGen is available as an online web-tool at https://zndraw.icp.uni-stuttgart.de/.
Installation
The package can be installed using pip
:
pip install simgen
Note that the analysis code requires the rdkit package, which is not installed by default. To install it, run
pip install simgen[all]
Usage
There are two main ways to use the package: via a command line interface or interactively using ZnDraw.
Interactive use
We host an online gpu-powered web-tool at https://zndraw.icp.uni-stuttgart.de/. The documentation for the web-tool is available here.
However, you can also run ZnDraw locally. After installing the package, you can run the following command to start the web-tool:
zndraw --port 1234 PATH_TO_XYZ_FILE # Path is optional; Use --no-browser for remote servers
# Do the next command in a separate terminal
simgen connect --device cuda # default port is 1234
If you want to try out linker generation, add the --add-linkers
flag to the simgen connect
command.
Run simgen connect --help
for more information.
[!TIP] SiMGen uses the
mace-models
package to download data and the hydrogenation model. Downloading local copies can speed up your workflow. To do so, rungit clone https://github.com/RokasEl/MACE-Models cd MACE-Models dvc pull simgen init . # or simgen init /path/to/MACE-ModelsThis will set SiMGen's default path to the local MACE models.
CLI use
For unconstrained generation, you can use the following command:
python scripts/generate_mols_cli.py --save-path PATH_TO_SAVE_MOLS \
--num-molecules 10 \
--num-heavy-atoms 9 \
--track-trajectories \
--prior-gaussian-covariance 1. 1. 0.1 # controls the shape of the prior
To construct molecules with more complicated shapes, you will have to manually define the shape via a point cloud prior. See scripts/paper_examples/generate_macrocycles.py
for an example.
References
If you use SiMGen in your research, please cite the following paper:
License
The code is licensed under the MIT license. See LICENSE
for more information.
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