Classification is an important task in data analysis and learning. Classification can be performed using supervised or unsupervised methods. From the unsupervised point of view, Formal Concept Analysis (FCA) can be used for such a task in an efficient and well-founded way. From the supervised point of view, emerging patterns rely on pattern mining and can be used to characterize classes of objects w.r.t. a priori labels. In this paper, we present a hybrid classification method which is based both on supervised and unsupervised aspects. This method relies on FCA for building a concept lattice and then detects the concepts whose extents determines classes of objects sharing the same labels. These classes can then be used as reference classes for classifying unknown objects. This hybrid approach has been used in an experiment in chemistry for classifying inhibitors of the c-Met protein which plays an important role in protein interactions and in the development of cancer.