论文标题
Molgraph：用于实现分子图和图形神经网络的Python包装，并具有张量
MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras
论文作者
论文摘要
事实证明，分子机器学习（ML）对于解决各种分子问题很重要，例如基于分子描述符或指纹预测分子特性。自最近以来，已经针对分子ML实现了图神经网络（GNN）算法，显示出与基于描述符或基于指纹的方法相当或出色的性能。尽管存在各种工具和包装在分子ML中应用GNN，但在这项工作中开发了一种名为Molgraph的新GNN包，其动机是创建与Tensorflow和Tensorflow和Keras应用程序编程界面（API）高度兼容的GNN模型管道的动机。 Molgraph还实现了化学模块，以适应小分子图的产生，可以将其传递给GNN算法以解决分子ML问题。为了验证GNN，它们对分子数据集以及三个色谱保留时间数据集进行了基准测试。这些基准的结果表明，GNN按预期进行。此外，GNN被证明可用于分子鉴定和改善色谱保留时间数据的可解释性。 Molgraph可从https://github.com/akensert/molgraph获得。可以在https://molgraph.readthedocs.io/en/latest/上找到安装，教程和实现详细信息。
Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a chemistry module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, they were benchmarked against the datasets of MoleculeNet, as well as three chromatographic retention time datasets. The results on these benchmarks illustrate that the GNNs performed as expected. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph. Installation, tutorials and implementation details can be found at https://molgraph.readthedocs.io/en/latest/.
