评估用于预测闪点的基于图的深度学习模型
Assessing Graph-based Deep Learning Models for Predicting Flash Point
摘要：
有机分子的闪点在防止易燃性危害方面发挥着重要作用，尽管数百万种化合物仍未测量，但存在大量测量值数据库。为了快速扩展现有数据到新化合物，许多研究人员使用定量结构-性质关系（QSPR）分析来有效地预测闪点。近年来，基于图的深度学习（GBDL）已成为传统QSPR的强大替代方法。本文首次将GBDL模型应用于闪点预测。我们评估了两个GBDL模型，消息传递神经网络（MPNN）和图卷积神经网络（GCNN）的性能，通过比较的方法。我们的研究结果表明，MPNN都优于GCNN和产量略差，但与以前的QSPR研究的性能相当。MPNN的平均R2和平均绝对误差（MAE）分数分别比以前的可比研究低2.3%和高2.0 K。为了进一步探索GBDL模型，我们收集了迄今为止最大的闪点数据集，其中包含10575个独特的分子。优化后的MPNN在完整的数据集上给出了0.803的测试数据R2和17.8 K的MAE。我们还从基于分子类型（酸，有机金属，有机锗，有机硅和有机锡）的综合数据集中提取了5个数据集，并探讨了这些类别中模型的质量。
英文摘要：
Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message-passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.against 12 previous QSPR studies using more traditional
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