Pro Generative Tensorial Reinforcement Learning (proGENTRL)

This is a Pytorch Lightning implementation of GENTRL. Recently Pytorch Lightning has gained much popularity due to its lightweight framework and flexibility with handling multiple devices from a single GPU to a HPC cluster. So in order to maximize the efficiency of using GENTRL on multi-GPU environment I have implemented this pytorch lightning code.

What is GENTRL?

The GENTRL model is a variational autoencoder with a rich prior distribution of the latent space. We used tensor decompositions to encode the relations between molecular structures and their properties and to learn on data with missing values. We train the model in two steps. First, we learn a mapping of a chemical space on the latent manifold by maximizing the evidence lower bound. We then freeze all the parameters except for the learnable prior and explore the chemical space to find molecules with a high reward.

Installation

Step 1 :

Make a new conda environment and install RDKit.

conda create -c rdkit -n my-rdkit-env rdkit

Then activate this new environment.

conda activate my-rdkit-env

Note : Make sure that the python3 version is 3.5 or higher and pip3 is installed

Step 2 :

Inside this environment install proGENTRL.

Using pip (Recommended)

pip install progentrl

Using source

git clone https://github.com/Bibyutatsu/proGENTRL.git
cd proGENTRL
python3 setup.py install

Step 3 :

Then I will suggest you install pytorch's latest version according to your cuda version (For e.g. 10.2)

python3 -m pip uninstall torch torchvision
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Step 4 : (Optional)

Making a new Kernel for jupyter notebook is recommended. For making a new kernel please follow these steps.

python3 -m pip install ipykernel
python3 -m ipykernel install --user --name rdkit_kernel

Now when you open jupyter notebook. Go to Change Kernel > rdkit_kernel

With these the installation is over and now we are ready to run the Example.ipynb notebook provided in the Repo.

Example.ipynb

This notebook is a good starting point to look at how proGENTRL works. It incorporates all the steps from VAE Training to Reinforcement Learning and finally Sampling or generation of new molecules in the form of SMILES strings.

The basic flow of the model is: VAE Train :arrow_right: Reinforcement Learning :arrow_right: Sampling

Below we show examples of generated molecules (more samples here). You can find more explanations in my originarlly forked Repo of GENTRL please visit here

For reading more visit my blog

Supporting Information for the paper "Deep learning enables rapid identification of potent DDR1 kinase inhibitors". Original Repo