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openchem_manuscript_preprint.pdf (705.92 kB)
OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
submitted on 22.07.2020 and posted on 23.07.2020by 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.