Consensus Ranking Approach to Understanding the Underlying Mechanism With QSAR

Constructing a highly predictive model and exploiting the underlying mechanism associated with a specific property of chemicals are the two main goals of quantitative structure-activity relationship analysis (QSAR). However, the latter has long been carried out as a byproduct of model construction. Here we confirmed for the first time in this study that conventional descriptor selection methods designed to develop a best predictive model are likely not suitable for mechanistic analysis, i.e., the selected descriptors strongly depended on the selection of chemicals in the training sets. As an alternative, a consensus ranking protocol was proposed to select a robust descriptor set for mechanistic analysis, which can successfully overcome the above shortcoming. Moreover, the consistently inferior model performance using descriptors selected for mechanistic analysis suggested the irreplaceable role of model development in achieving models with the best predictive capability.