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

• 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 documentationhere.

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

• 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:

• Modern NVIDIA GPU,compute capability 3.5 or newer.
• Python 3.5 or newer (we recommendAnaconda distribution)
• CUDA 9.0 or newer

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

git clone https://github.com/Mariewelt/OpenChem.gitcd OpenChemconda create --name OpenChem python=3.7conda activate OpenChemconda install --yes --file requirements.txtconda install -c rdkit rdkit nox cairoconda install pytorch torchvision -c pytorchpip install -e.

If your CUDA version is older than 9.0, checkPytorch website for different installation instructions.

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.

If you use OpenChem in your projects, please cite:

Korshunova, Maria, et al. "OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design." Journal of Chemical Information and Modeling 61.1 (2021): 7-13.

MolecularRNN model paper:

Popova, Mariya, et al. "MolecularRNN: Generating realistic molecular graphs with optimized properties." arXiv preprint arXiv:1905.13372 (2019).

OpenChem was supported byCarnegie Mellon University,the University of North Carolina at Chapel Hill andNVIDIA Corp.