论文标题
图形神经网络预测底物特异性有机反应条件
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
论文作者
论文摘要
我们提出了使用图神经网络（GNN）进行系统研究，以建模有机化学反应。为此，我们准备了来自有机化学文献的四个无处不在反应的数据集集合。我们评估了七个不同的GNN体系结构，用于与实验试剂和条件鉴定有关的分类任务。我们发现模型能够识别影响反应条件并导致准确预测的特定图形特征。这里的结果在推进分子机器学习方面表现出了巨大的希望。
We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.
