Reference

This repository contains the code associated with our paper:

Paul Maragakis, Hunter Nisonoff, Brian Cole, and David E. Shaw, "A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery," Journal of Chemical Information and Modeling, vol. 60, no. 10, 2020, pp. 4487–4496. Text

The corresponding dataset can be found on our web site.

Installing and setup

To train your own data you'll need a GPU compatible with CUDA 10.1. You can run without a GPU using our pre-trained data set, but performance will be lower than with a GPU.

You will need a compatible cuDNN and

python >=3.7, <=3.9
pytorch >= 1.0
fastai == 1.0.55
scipy
numpy
rdkit

The tests require pytest and hypothesis.

For the provided sample notebooks you will also need:

jupyter
matplotlib
seaborn
sentencepiece

note: The script download_drd2_dataset.sh requiresrdkit==2018.09.01 and scikit-learn==0.19.2.

Conda

For easy installation, we've also provided a conda environment.yml. Refer to the miniconda documentation for instructions for installing conda. The conda environment is all that is required for CPU applications. For GPU applications, the environment is limited to CUDA 10. Running DESMILES on GPUs not compatible with CUDA 10 requires building pytorch 1.0.0 from source.

Containers

We're including a Dockerfile to build a containerized, GPU-enabled version of DESMILES. You can build a docker image by running:

docker build -t desmiles:1.0 https://github.com/DEShawResearch/DESMILES.git#1.0

Using

DESMILES identifies the data directory with the environment variable DESMILES_DATA_DIR. Set it with

export DESMILES_DATA_DIR=<Path/To/DESMILES/data>

Where DESMILES/data is the unpacked form of the data set.

If you are using a container, you'll need to make the data directory visible within the container and correspond to the environment variable DESMILES_DATA_DIR within the container. The provided Dockerfile defaults DESMILES_DATA_DIR such that the following bind mount will work: -v <Path/to/DESMILES/data>:/desmiles/data

Jupyter notebooks

We provide two demo Jupyter notebooks:

• intro_demo_of_DESMILES.ipynb shows simple ways to use a pretrained model to generate molecules.
• overview_of_DESMILES.ipynb shows examples of training a simple model and fine-tuning an existing model.

The overview_of_DESMILES.ipynb notebook demonstrates how to sample potential potent binders to DRD2 using the benchmark data set published by Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola. We've provided a script to download and preprocess the data download_drd2_dataset.sh.

If you are using a container, don't forget to include port forwarding in the container and Jupyter:

• docker run -p 8888:8888 -it --rm --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -v <Path/to/DESMILES/data>:/desmiles/data desmiles:1.0

• jupyter notebook --ip "0.0.0.0" --no-browser --allow-root

• edit the URL accordingly and access from a local browser