Learning Deep Architectures for Interaction Prediction in   Structure-based Virtual Screening
  We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E and PDBBind databases. 
Adam Gonczarek, Jakub M. Tomczak, Szymon Zar\k{e}ba, Joanna Kaczmar,   Piotr D\k{a}browski, Micha{\l} J. Walczak
stat.ML cs.LG