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OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
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benchmark_datasets added tox21 and logP datasets Aug 9, 2018
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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.


Check out OpenChem documentation here.

Supported functionality


  • 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


  • 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.



In order to get started you need:

General installation

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

git clone
cd 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.


OpenChem is sponsored by the University of North Carolina at Chapel Hill and NVIDIA Corp.


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