Deep neural networks (DNNs) based quantitative structure–property relationship (QSPR) studies are receiving increasing attention due to their excellent performances. A systematic methodology coupling multiple machine learning technologies is proposed to systematically solve vital problems including applicability domain and prediction uncertainty in DNN-based QSPR modeling. Key features are rapidly extracted from plentiful but chaotic descriptors by principal component analysis (PCA) and kernel PCA. Then, a detailed applicability domain (AD) is defined by K-means algorithm to avoid unreliable predictions and discover its potential impact on prediction uncertainty. Moreover, prediction uncertainty is analyzed with dropout-embedded DNN by thousands of independent tests to assess the reliability of predictions. The prediction of flashpoint temperature is employed as a case study, demonstrating that the model accuracy is remarkably improved comparing with the referenced model. Furthermore, the proposed methodology breaks through difficulties in analyzing the uncertainty of DNN-based QSPRs and presents an AD correlated with the uncertainty.

中文翻译：

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一种基于深度神经网络的结构-性能关系的系统建模方法，用于快速可靠地预测闪点

基于深度神经网络 (DNN) 的定量结构-性质关系 (QSPR) 研究因其出色的性能而受到越来越多的关注。提出了一种耦合多种机器学习技术的系统方法，以系统地解决基于 DNN 的 QSPR 建模中的适用性域和预测不确定性等重要问题。通过主成分分析 (PCA) 和核 PCA，可以从大量但混乱的描述符中快速提取关键特征。然后，通过 K-means 算法定义详细的适用性域 (AD)，以避免不可靠的预测并发现其对预测不确定性的潜在影响。此外，预测的不确定性是用dropout分析的-通过数以千计的独立测试嵌入 DNN，以评估预测的可靠性。以闪点温度的预测作为案例研究，表明与参考模型相比，模型精度有显着提高。此外，所提出的方法突破了分析基于 DNN 的 QSPR 的不确定性的困难，并提出了与不确定性相关的 AD。