https://github.com/ardigen/mol-cycle-gan

Mol-CycleGAN - a generative model for molecular optimization
https://github.com/ardigen/mol-cycle-gan

Last synced: 3 months ago
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Mol-CycleGAN - a generative model for molecular optimization

• Host: GitHub
• URL: https://github.com/ardigen/mol-cycle-gan
• Owner: ardigen
• License: mit
• Created: 2019-02-01T14:29:03.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-02-06T14:30:24.000Z (almost 6 years ago)
• Last Synced: 2024-06-29T12:35:01.972Z (5 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 338 KB
• Stars: 75
• Watchers: 3
• Forks: 37
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Mol-CycleGAN - a generative model for molecular optimization

Official implementation of Mol-CycleGAN for molecular optimization.

Keras CycleGan implementation is based on [tjwei/GANotebooks].

## Requirements

We highly recommend to use conda for package management -- the `environment.yml` file is provided.

The environment can be created by running:

```

conda env create -f environment.yml

```

We use Junction Tree Variational Autoencoder implementation as a submodule in Mol-CycleGAN code.

After cloning this repo, the following script should be executed before running the code

```

./scripts/init_repo.sh 

```

## Datasets

We provide the user with all datasets needed to reproduce the aromatic rings experiments.

Downloading all the input data (ZINC 250k dataset and related JT-VAE encodings) can be performed by running:

```

./scripts/download_input_data.sh

```

Downloading all the data from aromatic rings experiments (train / test splits of datasets, molecules returned by Mol-CycleGAN and related SMILES) can be performed by running:

```

./scripts/download_ar_data.sh

```

## Basic use

This code is an implementation of CycleGan for molecular optimization.

Training of the model can be performed by running:

```

python train.py

```

with specified training parameters.

After the model is trained and the test set translation is generated, for decoding the molecules the JT-VAE code should be used. This can be performed by running:

```

python decode.py

```

with specified decoding parameters.

## Experiments

We provide all the data and code needed to reproduce the `Aromatic rings` experiment.

1. In `data/input_data/aromatic_rings/datasets_generator_aromatic_rings.ipynb` one can find the data factory - the code that is needed to create train and test sets used in the experiment.

2. Training of the model can be performed by running `./scripts/run_aromatic_rings_training.sh`. It calls the `train.py` function with base parameters, which are set to process the aromatic rings data.

3. Decoding the molecules can be performed by running `./scripts/run_aromatic_rings_decoding.sh`. It calls the `decode.py` function with base parameters, which are set to process the aromatic rings data.

4. The analysis of the output is provided in the notebook `experiments/aromatic_rings.ipynb`.

## Disclaimer

The code for Mol-Cycle-Gan was natively written in Python3, however, the JT-VAE package is written in Python2. To ensure the ease of use, we used downgraded versions of packages, so that the entire experiment can be run in a single environment.

Since many of those packages are outdated, we strongly recommend using the ```environment.yml``` file provided to construct the working environment.