DGL-LifeSci：生命科学中图形深度学习的开源工具包
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
摘要：
图神经网络（GNN）构成了用于图数据的一类深度学习方法。它们在化学和生物学中有着广泛的应用，如分子性质预测、反应预测和药物-靶标相互作用预测等。尽管有兴趣，但基于GNN的建模是具有挑战性的，因为除了编程和深度学习之外，它还需要图形数据预处理和建模。在这里，我们介绍了DGL-LifeSci，这是一个用于生命科学中图形的深度学习的开源软件包。DGL-LifeSci是一个基于RDKit，PyTorch和Deep Graph Library（DGL）的Python工具包。DGL-LifeSci允许在自定义数据集上进行基于GNN的建模，用于分子性质预测，反应预测和分子生成。通过其命令行界面，用户可以在没有任何编程和深度学习背景的情况下执行建模。我们使用标准基准测试MoleculeNet，USPTO和ZINC来测试命令行界面。与以前的实现相比，DGL-LifeSci的速度提高了6倍。对于建模灵活性，DGL-LifeSci为建模管道的各个阶段提供了优化的模块。此外，DGL-LifeSci提供了预训练模型，用于再现测试实验结果，并在没有训练的情况下应用模型。该代码在Apache-2.0许可证下分发，可在https://github.com/awslabs/dgl-lifesci上免费访问。
英文摘要：
Graph neural networks (GNNs) constitute a class of deep learning methods for graph data. They have wide applications in chemistry and biology, such as molecular property prediction, reaction prediction and drug-target interaction prediction. Despite the interest, GNN-based modeling is challenging as it requires graph data pre-processing and modeling in addition to programming and deep learning. Here we present DGL-LifeSci, an open-source package for deep learning on graphs in life science. DGL-LifeSci is a python toolkit based on RDKit, PyTorch and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based modeling on custom datasets for molecular property prediction, reaction prediction and molecule generation. With its command-line interfaces, users can perform modeling without any background in programming and deep learning. We test the command-line interfaces using standard benchmarks MoleculeNet, USPTO, and ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed up by up to 6x. For modeling flexibility, DGL-LifeSci provides well-optimized modules for various stages of the modeling pipeline. In addition, DGL-LifeSci provides pre-trained models for reproducing the test experiment results and applying models without training. The code is distributed under an Apache-2.0 License and is freely accessible at https://github.com/awslabs/dgl-lifesci.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
