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OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design

preprint
submitted on 22.07.2020 and posted on 23.07.2020 by Mariya Popova, Boris Ginsburg, Alexander Tropsha, Olexandr Isayev
Deep learning models have demonstrated outstanding results in many data-rich areas of research, such as computer vision and natural language processing. Currently, there is a rise of deep learning in computational chemistry and materials informatics, where deep learning could be effectively applied in modeling the relationship between chemical structures and their properties. With the immense growth of chemical and materials data, deep learning models can begin to outperform conventional machine learning techniques such as random forest, support vector machines, nearest neighbor, etc. Herein, we introduce OpenChem, a PyTorch-based deep learning toolkit for computational chemistry and drug design. OpenChem offers easy and fast model development, modular software design, and several data preprocessing modules. It is freely available via the GitHub repository.

Funding

NIH 1R01GM114015

ONR N00014-16-1-2311

NSF CHE-1802789

NVIDIA Graduate Fellowship

MolSSI Software Fellowship

History

Email Address of Submitting Author

olexandr@olexandrisayev.com

Institution

Carnegie Mellon University

Country

United States

ORCID For Submitting Author

0000-0001-7581-8497

Declaration of Conflict of Interest

N/A

Exports