This study is dedicated to an introduction of a novel method that automatically extracts potential structural alerts from a dataset of molecules. These triggering structures can be further used for knowledge discovery and for classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow which integrates a graph-mining tool guided by growth-rate and stability. The growth-rate is a well-established measurement of contrast between classes. Moreover, the extracted patterns correspond to formal concepts; the most robust patterns, named the stable emerging patterns (SEPs), can then be identified thanks to their stability, a new notion originating from the domain of Formal Concept Analysis. All these elements are explained in the paper from the point of view of computation. The method was applied on a molecular dataset on mutagenicity. The experimental results demonstrate its efficiency: it automatically outputs a manageable amount of structural patterns that are strongly related to mutagenicity. Moreover, a part of the resulting structures corresponds to already known structural alerts. Finally, an in-depth chemical analysis relies on these structures, it demonstrates how the method can initiate promising processes of chemical knowledge discovery.