Quantitative structure-property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software

摘要

The soil adsorption coefficient (K_(oc)) is an environmental fate parameter that is essential for environmental risk assessment. However, obtaining K_(oc) requires a significant amount of time and enormous expenditure. Thus, it is necessary to efficiently estimate K_(oc) in the early stages of a chemical's development. In this study, a quantitative structure-property relationship (QSPR) model was developed using calculated physicochemical properties and molecular descriptors with the OPEn structure-activity/property Relationship App (OPERA) and Mordred software using the largest available K_(oc) dataset. Specifically, we compared the accuracies of the model using the light gradient boosted machine (LightGBM), a gradient boosting decision tree (GBDT) algorithm, with those of previous models. The experimental results suggested the potential to develop a QSPR model that will produce highly accurate K_(oc) values using molecular descriptors and physicochemical properties. Unlike previous studies, the use of a combination of LightGBM, OPERA and Mordred enables the prediction of K_(oc) for many chemicals with high accuracy. In this study, OPERA was used to calculate the physicochemical properties, and Mordred was used to calculate molecular descriptors. The wide range of chemicals covered by OPERA and Mordred enables the analysis of a diverse range of chemical compounds. We also report a method to tune the LightBGM program. The use of fast-processing software, such as LightGBM, enables parameter tuning of a method required to obtain best performance. Our research represents one of the few studies in the field of environmental chemistry to use LightGBM. Using physicochemical properties as well as molecular descriptors, we could develop highly accurate K_(oc) prediction models when compared to prior studies. In addition, our QSPR models may be useful for preliminary environmental risk assessment without incurring significant costs during the early chemical developmental stage.