OpenChem

OpenChem is a deep learning toolkit for Computational Chemistry with PyTorch backend. The goal of OpenChem is to make Deep Learning models an easy-to-use tool for Computational Chemistry and Drug Design Researchers.

Main features

Modular design with unified API, modules can be easily combined with each other.
OpenChem is easy-to-use: new models are built with only configuration file.
Fast training with multi-gpu support.
Utilities for data preprocessing.
Tensorboard support.

Documentation

Check out OpenChem documentation here.

Supported functionality

Tasks:

Classification (binary or multi-class)
Regression
Multi-task (such as N binary classification tasks)

Data types

Sequences of characters such as SMILES strings or amino-acid sequences
Molecular graphs. OpenChem takes care of converting SMILES strings into molecular graphs

Modules:

Token embeddings
Recurrent neural network encoders
Graph convolution neural network encoders
Multi-layer perceptrons

We are working on populating OpenChem with more models and other building blocks.

Installation

Requirements

In order to get started you need:

Modern NVIDIA GPU, compute capability 3.5 or newer.
Python 3.6 (we recommend Anaconda distribution)
CUDA 9.0
Pytorch 0.4.1
Tensorflow 1.8.0 with GPU support
RDKit
Scikit-learn
Numpy

General installation

If you installed your Python with Anacoda you can run the following commands to get started:

git clone https://github.com/Mariewelt/OpenChem.gitcd Openchem
conda install --yes --file requirements.txt
conda install -c rdkit rdkit nox cairo
conda install pytorch torchvision -c pytorch
pip install tensorflow-gpu

If your CUDA version is other than 9.0, check Pytorch and Tensorflow websites for different installation instructions.

Installation with Docker

Alternative way of installation is with Docker. We provide a Dockerfile, so you can run your models in a container that already has all the necessary packages installed. You will also need nvidia-docker in order to run models on GPU.