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引用次数: 0
摘要
自燃温度(AIT)是评估化学物质潜在危害的重要指标。为了更深入地了解模型性能,并促进建立自燃温度预测的有效方法,本研究对自燃温度的定量结构-性能关系(QSPR)建模进行了基准调查。作为这项工作的新颖之处,在 AIT 建模过程中实现了三项重大进展,包括明确考虑数据质量、利用最先进的特征工程工作流程,以及首次在 AIT 预测中采用基于图的深度学习技术的创新应用。具体来说,除了对采用消息传递神经网络架构的深度学习模型进行评估外,还对三种传统的 QSPR 模型(多线性回归、支持向量回归和人工神经网络)以及图形数据增强技术进行了评估。
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.
期刊介绍:
SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.