We investigate the graph-based convolutional neural network approach for
predicting and ranking gas adsorption properties of crystalline Metal-Organic
Framework (MOF) adsorbents for application in post-combustion capture of
$\textrm{CO}_2$. Our model is based solely on standard structural input files
containing atomistic descriptions of the adsorbent material candidates. We
construct novel methodological extensions to match the prediction accuracy of
classical machine learning models that were built with hundreds of features at
much higher computational cost. Our approach can be more broadly applied to
optimize gas capture processes at industrial scale.

该研究利用基于图的卷积神经网络模型进行预测和排名晶体金属有机骨架 (MOF) 吸附剂的气体吸附性能，其模型仅基于包含吸附剂材料候选物的标准结构输入文件。结果表明，该方法可以更精确地预测吸附性能，在运算速度更快、特征更少的情况下不失精度，更广泛地应用于工业规模下的气体捕捉过程的优化中。

利用图神经网络预测纳米孔中的 $	extrm {CO}_2$ 吸附

Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural  Networks

Tabular data is a crucial form of information expression, which can organize
data in a standard structure for easy information retrieval and comparison.
However, in financial industry and many other fields tables are often disclosed
in unstructured digital files, e.g. Portable Document Format (PDF) and images,
which are difficult to be extracted directly. In this paper, to facilitate deep
learning based table extraction from unstructured digital files, we publish a
standard Chinese dataset named FinTab, which contains more than 1,600 financial
tables of diverse kinds and their corresponding structure representation in
JSON. In addition, we propose a novel graph-based convolutional neural network
model named GFTE as a baseline for future comparison. GFTE integrates image
feature, position feature and textual feature together for precise edge
prediction and reaches overall good results.

本文提出了 FinTab 数据集，包含 1600 个财务表以及它们在 JSON 表示中的结构，以方便从非结构化数字文件中提取表格。同时，作者还提出了一种基于 GFTE 的图卷积神经网络模型，它将图像特征、位置特征和文本特征集成在一起，用于精确的边缘预测，并取得了总体上不错的结果。

基于图的财务报表提取

GFTE: Graph-based Financial Table Extraction

We present a simple, modular graph-based convolutional neural network that
takes structural information from protein-ligand complexes as input to generate
models for activity and binding mode prediction. Complex structures are
generated by a standard docking procedure and fed into a dual-graph
architecture that includes separate sub-networks for the ligand bonded topology
and the ligand-protein contact map. This network division allows contributions
from ligand identity to be distinguished from effects of protein-ligand
interactions on classification. We show, in agreement with recent literature,
that dataset bias drives many of the promising results on virtual screening
that have previously been reported. However, we also show that our neural
network is capable of learning from protein structural information when, as in
the case of binding mode prediction, an unbiased dataset is constructed. We
develop a deep learning model for binding mode prediction that uses docking
ranking as input in combination with docking structures. This strategy mirrors
past consensus models and outperforms the baseline docking program in a variety
of tests, including on cross-docking datasets that mimic real-world docking use
cases. Furthermore, the magnitudes of network predictions serve as reliable
measures of model confidence

提出了一种基于图的卷积神经网络，该网络以蛋白质 - 配体复合物的结构信息作为输入，生成活性和结合模式预测模型，并开发了一种深度学习模型，用于结合模式预测，并在各种测试中优于基线对接程序。