Decision forest—a machine learning algorithm for QSAR modeling

There are a huge number of chemicals that have been introduced at the market and in the environment. Safety evaluation of chemical-containing products at the market and risk assessment of chemicals in the environment are necessary for protecting public health. However, experimentally evaluating toxicity potential for all chemicals is extremely difficult. Therefore, computational methods such as quantitative structure-activity relationship (QSAR) modeling have been used to facilitate safety evaluation and risk assessment. Machine learning algorithms are necessary for QSAR modeling. Similar to random forest, Decision forest is a consensus machine learning algorithm using decision trees as member models. It uses less but diverse decision trees and needs only a small number of independent variables. Thus, it is suitable for QSAR modeling. This chapter introduces the algorithm and features of decision forest. We also demonstrate its usefulness in applications of QSAR modeling by reviewing some cases of applications to both two-class and multiclass QSAR modeling.