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

SAR QSAR Environ Res. 2024 Feb 19:1-20. doi: 10.1080/1062936X.2024.2312527. Online ahead of print.ABSTRACTThe 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.PMID:38372083 | DOI:10.1080/1062936X.2024.2312527

Source: SAR and QSAR in Environmental Research - February 19, 2024 Category: Environmental Health Authors: J Chen L Zhu J Wang Source Type: research