Pollution risk assessment by designing predictive binary classification models of substituted benzenes centered on data mining and machine learning techniques.

There is a growing need for industry and global regulatory agencies to develop rapid chemical safety assessment through more reliable theoretical models. Thus, quantitative structure-toxicity relationship (QSTR) models are preferred by regulators to bring chemicals to market rather than long and expensive animal testing. In this study, we evaluated four binary classification machine learning (ML) models (support vector machine, k-nearest neighbor, CART decision tree and random forest) for their ability to predict toxicity towards Tetrahymena pyriformis using 1416 benzene-derived compounds (749 chemicals evaluated and 697 synthetic toxicants) classified into two groups: non-toxic molecules (NTox) with 708 observations and toxic molecules (Tox) with 708 observations.