An Imbalance Regression Approach to Toxicity Prediction of Chemicals for Potential Use in Environmentally Acceptable Lubricants.

Abstract

Lubricants are complex mixtures of chemicals that help machines function at the right level of friction and wear. Lubricant formulation methods are based on empirical experience of chemical substances that have been used as lubricants for decades. In the last years, the discussion about their environmental problem has triggered new legislations resulting in the search for Environmentally Acceptable Lubricants, which should be biodegradable, minimally toxic, and nonbioaccumulative. Finding new chemicals that comply with these three criteria is a long and expensive process that can be boosted by machine learning (ML). In this paper, we are addressing toxicity prediction with machine learning models by exploring the application of ensemble learners to chemicals having imbalanced data distribution. We investigated the effectiveness of sampling techniques to balance the data and improve the performance of the ensemble learning model. The model can predict toxicity for nonundersampled groups, which in our case corresponds to the moderately to highly toxic groups. The results of this work are useful for lubricant formulators since regulations accept moderate-to-highly toxic chemicals in lubricants if their concentration is below 20 wt %.