Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer

To develop predictive models for the reactivity of organic contaminants toward four oxidants-SO4 center dot-, HClO, O-3, and ClO2-all with small sample sizes, we proposed two approaches: combining small data sets and transferring knowledge between them. We first merged these data sets and developed a unified model using machine learning (ML), which showed better predictive performance than the individual models for HClO (RMSEtest: 2.1 to 2.04), O-3 (2.06 to 1.94), ClO2 (1.77 to 1.49), and SO4 center dot- (0.75 to 0.70) because the model corrected the wrongly learned effects of several atom groups. We further developed knowledge transfer models for three pairs of the data sets and observed different predictive performances: improved for O-3 (RMSEtest: 2.06 to 2.01)/HClO (2.10 to 1.98), mixed for O-3 (2.06 to 2.01)/ClO2 (1.77 to 1.95), and unchanged for ClO2 (1.77 to 1.77)/HClO (2.1 to 2.1). The effectiveness of the latter approach depended on whether there was consistent knowledge shared between the data sets and on the performance of the individual models. We also compared our approaches with multitask learning and image-based transfer learning and found that our approaches consistently improved the predictive performance for all data sets while the other two did not. This study demonstrated the effectiveness of combining small, similar data sets and transferring knowledge between them to improve ML model performance.

Automated summary

The study demonstrated the effectiveness of combining small, similar data sets and transferring knowledge between them to improve the predictive performance of machine learning models.