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https://github.com/yunjaechoi/vaemols
Variational Autoencoder for Molecules
https://github.com/yunjaechoi/vaemols
molecule rdkit tensorflow variational-autoencoder
Last synced: 2 months ago
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Variational Autoencoder for Molecules
- Host: GitHub
- URL: https://github.com/yunjaechoi/vaemols
- Owner: YunjaeChoi
- License: mit
- Created: 2019-01-02T13:23:34.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-01-02T15:36:51.000Z (almost 6 years ago)
- Last Synced: 2024-09-29T13:43:28.376Z (3 months ago)
- Topics: molecule, rdkit, tensorflow, variational-autoencoder
- Language: Jupyter Notebook
- Size: 27.2 MB
- Stars: 31
- Watchers: 1
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
Variational Autoencoder for Molecules
***********************************************
Variational autoencoder for molecules in tensorflow.
Dependencies
============
1. Rdkit
.. code:: shell
conda install -c rdkit rdkit
2. Tensorflow
cpu-version
.. code:: shell
pip install tensorflow
gpu-version
.. code:: shell
pip install tensorflow-gpu
Preprocessing
=============
1. Data
-------
`ChEBML 24 Database `_
was used for SMILES data.
SMILES strings were padded with spaces to max_len(default=120) and strings larger than max_len were discarded. Remaining strings are labeled character by character(max_len labels in one string).
2. preprocess.py
----------------
Does the following steps:
1. Downloads `chembl_24_1_chemreps.txt.gz `_
2. Preprocess SMILES strings
3. Saves processed data into numpy arrays.
Numpy arrays contains training data, testing data, dictionaries for character <-> label(integer) interchange.
Training
========
1. Model
--------
Model consists of CNN encoder and CuDNNGRU decoder and defined in
`vae.py `_
2. train.py
-----------
Does the following steps:
1. Loads preprcessed data
2. trains with fit_generator using DataGenerator
Notebooks
=========
Notebooks are here to help after training is done.
1. `structure_variation.ipynb `_
-------------------------------------------------------------------------------------------------------------
This notebook helps to get variational structures when given a SMILES string.
2. `visualize_latent_space.ipynb `_
-------------------------------------------------------------------------------------------------------------------
This notebook helps visualizing learned latent space using a plot or tensorboard.
tensorboard visualization example:
.. image:: https://raw.githubusercontent.com/YunjaeChoi/vaemols/master/doc/image/tensorboard.png
3. `find_top_k_mols_in_latent_space.ipynb `_
-------------------------------------------------------------------------------------------------------------------------------------
This notebook helps to get top_k similar molecules measured by euclidean distance in latent space.