Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis

The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.

自燃温度（autoignition temperature, AIT）是评估化学物质潜在危害的关键指标。为深入解析模型性能并建立高效的AIT预测方法学体系，本研究针对用于AIT预测的定量构效关系（Quantitative Structure-Property Relationship, QSPR）建模开展基准研究。本研究的创新之处在于，在AIT建模流程中实现了三项关键改进：明确考量数据质量、采用前沿特征工程工作流，以及首次将基于图的深度学习技术应用于AIT预测任务。具体而言，本研究评估了三类传统QSPR模型：多元线性回归（multi-linear regression）、支持向量回归（support vector regression）与人工神经网络（artificial neural networks）；同时还对采用消息传递神经网络（message passing neural network）架构并辅以图数据增强（graph-data augmentation）技术的深度学习模型进行了性能测评。