Awesome
stack_CVAE
We designed a novel generative model, Stack-CVAE, that combines stack-RNN with CVAE to generate structural expressions in SMILES. Stack-CVAE is based on CVAE, which produces substances similar to, but not the same as, the substances used for training. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an agent in reinforcement learning so that the resulting chemical formulas have the desired chemical properties and show high binding affinity with specific target proteins.
Trin environment
installation with anaconda
<pre>
<code>
# Clone the reopsitory to your desired directory
$ git clone https://github.com/HwanheeKim813/stack_CVAE.git
$ cd stack_CVAE
#install data
$ wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1CvmznJFNiMu_k20MKKtdV0hL4v2KogVe' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1CvmznJFNiMu_k20MKKtdV0hL4v2KogVe" -O data.zip && rm -rf /tmp/cookies.txt
#unzip data
$ unzip data.zip
# Create new conda environment with Python 3.7
$ conda create -n stack_CVAE python=3.7
# Activate the environment
$ conda activate stack_CVAE
# Install pip dependencies
$ pip install -r requirements.txt
# Install conda dependencies
$ conda install -c rdkit rdkit nox cairo
$ conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
# Install RAscore predictor
$ git clone https://github.com/reymond-group/RAscore.git
$ cd RAscore
$ pip install --editable .
$ cd RAscore/models
$ unzip models.zip
cd ../../..
</code>
</pre>
Run
<pre>
<code>
$ python stackCVAE_rein_train.py
</code>
</pre>
Test
<pre>
<code>
$ python generate_sample.py -h
usage: generate_sample.py [-h] [-c condition] [-d Drug] [-s sample] [-p path] [-n models name] [-ss save sample] [-sf save sample graph figure]
optional arguments:
-h, --help show this help message and exit
-c condition Properties for Generate, [MW,logP,TPSA]
-d Drug A Drug Name for Generate
-s sample Number of samples want to generate sample.
-p path model's path want to generate sample.
-n models name model's name want to generate sample.
-ss save sample save samples.
-sf save sample graph figure save samples graph.
$ python generate_sample.py
$ python sample_prop_check.py
</code>
</pre>