OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
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DOI：
10.1021/acs.jcim.0c00971
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发表时间：
2021-01-04
影响因子：
5.6
通讯作者：
Isayev, Olexandr
中科院分区：
文献类型：
--
作者：
Korshunova, Maria;Ginsburg, Boris;Isayev, Olexandr
深度学习模型在许多数据丰富的研究领域（如计算机视觉和自然语言处理）都取得了杰出的成果。目前，深度学习在计算化学和材料信息学中兴起，深度学习可以有效地应用于化学结构与其性质之间的关系建模。随着化学和材料数据的巨大增长，深度学习模型可以开始超越传统的机器学习技术，如随机森林、支持向量机和最近邻。在这里，我们介绍OpenChem，一个基于PyTorch的深度学习工具包，用于计算化学和药物设计。OpenChem提供简单快速的模型开发、模块化软件设计和多个数据预处理模块。它可以通过GitHub仓库免费获得。
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, and nearest neighbor. 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.