  Structure-based drug design involves finding ligand molecules that exhibit
structural and chemical complementarity to protein pockets. Deep generative
methods have shown promise in proposing novel molecules from scratch (de-novo
design), avoiding exhaustive virtual screening of chemical space. Most
generative de-novo models fail to incorporate detailed ligand-protein
interactions and 3D pocket structures. We propose a novel supervised model that
generates molecular graphs jointly with 3D pose in a discretised molecular
space. Molecules are built atom-by-atom inside pockets, guided by structural
information from crystallographic data. We evaluate our model using a docking
benchmark and find that guided generation improves predicted binding affinities
by 8% and drug-likeness scores by 10% over the baseline. Furthermore, our model
proposes molecules with binding scores exceeding some known ligands, which
could be useful in future wet-lab studies.
