Perturbation Modeling Framework for Novel Design of SRC Kinase Inhibitors

Verkhivker Lab at Chapman University

Authors:

Steven Agajanian
Ryan Kassab
Keerthi Krishnan
Gennady Verkhivker

This repository contains the framework and code for instilling molecular transformations of kinase inhibitors using generative learning methodologies and targeted perturbation modeling. The software written is a combination of utilizing the ChemVAE architecture developed by Jennifer Wei, Benjamin Sanchez-Lengeling, Dennis Sheberla, Rafael Gomez-Bomberelli, and Alan Aspuru-Guzik (https://github.com/aspuru-guzik-group/chemical_vae/blob/master/aux_data/banner.png?raw=true)[doi:10.1021/acscentsci.7b00572](http://pubs.acs.org/doi/abs/10.1021/acscentsci.7b00572) as well as perturbation modeling architecture developed by the Verkhivker Lab at Chapman University to perform our molecular transformation experiments. Below is a brief description of the folders and files present in the repository.

Files/Folders:

perturbation_experiment.ipynb file: Notebook containing the software for the use of variational autoencoder, the perturbation modeling experiments, and the post processing analysis
Fingerprinting_Similarity.ipynb: Notebook containing examples of similarity testing and implementation
chemvae and models: Folders containing the library of models and architecture used to build and compile ChemVAE (description of model files below)
data: data folder containing molecular data of our kinase inhibitors and GDB small molecule baseline retrieved from ZINC and GDB-17 database in SMILES format
examples: folder containing intro_to_chemvae.ipynb, a notebook with starter code on use of variational autoencoder
graphs: folder containing graphs for different stages of analysis in pipeline
results: folder containing files of any raw and curated generated data from experimentation
supplemental_info: Folder containing supplemental information of experiment results, such as final similarity data of generated molecules to known SRC Inhibitors, graphs represented chemical analysis of generated data, graphs containing analysis of chemical properties from initial data, and preliminary generated data, and a suppemental information guide.
environment.yml: environment file to help set up ChemVAE environment with proper dependencies and tools
requirements.txt: text file stating dependencies for certain packages in environment set-up

To have the proper set-up for the variational autoencoder framework, please refer to the directions on ChemVAE below and refer to their github link (https://github.com/aspuru-guzik-group/chemical_vae/blob/master/aux_data/banner.png?raw=true) and paper (http://pubs.acs.org/doi/abs/10.1021/acscentsci.7b00572) for more information on ChemVAE:

ChemVAE Framework Set Up (information provided by https://github.com/aspuru-guzik-group/chemical_vae/issues/new):

Questions, problems?

Make a github issue 😄. Please be as clear and descriptive as possible.

How to install

Requirements:

An Anaconda python environment is recommend. Check the environment.yml file, but primarily:

Python >= 3.5
Keras >= 2.0.0 && <= 2.0.7
Tensorflow == 1.1
RDKit
Numpy

Jupyter notebook is required to run the ipynb examples. Make sure that the Keras backend is set to use Tensorflow

via Anaconda (recommended way)

Create a conda enviroment:

conda env create -f environment.yml
source activate chemvae
python setup.py install

via pip

Assuming you have all the requirements:

pip install git+https://github.com/aspuru-guzik-group/chemical_vae.git

Example: ZINC dataset

This repository contains an example of how to run the autoencoder on the zinc dataset.

First, take a look at the zinc directory. Parameters are set in the following jsons

exp.json - Sets parameters for location of data, global experimental parameters number of epochs to run, properties to predict etc.

For a full description of all the parameters, see hyperparameters.py ; parameters set in exp.json will overwrite parameters in hyperparameters.py, and parameters set in params.json will overwrite parameters in both exp.json and hyperparameters.py

Once you have set the parameters, run the autoencoder using the command from directory with exp.json:

python -m chemvae.train_vae

(Make sure you copy examples directories to not overwrite the trained weights (*.h5))

Components

train_vae.py : main script for training variational autoencoder Accepts arguments -d ... Example of how to run (with example directory here)

models.py - Library of models, contains the encoder, decoder and property prediction models.
tgru_k2_gpu.py - Custom keras layer containing custom teacher forcing/sampling
sampled_rnn_tf.py - Custom rnn function for tgru_k2_gpu.py, written in tensorflow backend.
hyperparameters.py - Some default parameter settings for the autoencoder
mol_utils.py - library for parsing SMILES into one-hot encoding and vice versa
mol_callbacks.py - library containing callbacks used by train_vae.py
- Includes Weight_Annealer callback, which is used to update the weight of the KL loss component
vae_utils.py - utility functions for an autoencoder object, used post processing.

Authors of ChemVAE:

This software is written by Jennifer Wei, Benjamin Sanchez-Lengeling, Dennis Sheberla, Rafael Gomez-Bomberelli, and Alan Aspuru-Guzik (alan@aspuru.com). It is based on the work published in https://arxiv.org/pdf/1610.02415.pdf by

Feel free to reach out to us with any questions!

kassabry/Perturbation_Experiment

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