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

23 July 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Deep Learning Applications
Deep Learning Framework
QSAR Modeling
generative models

Supplementary weblinks


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