  Generating molecules with high binding affinities to target proteins (a.k.a.
structure-based drug design) is a fundamental and challenging task in drug
discovery. Recently, deep generative models have achieved remarkable success in
generating 3D molecules conditioned on the protein pocket. However, most
existing methods consider molecular generation for protein pockets
independently while neglecting the underlying connections such as
subpocket-level similarities. Subpockets are the local protein environments of
ligand fragments and pockets with similar subpockets may bind the same
molecular fragment (motif) even though their overall structures are different.
Therefore, the trained models can hardly generalize to unseen protein pockets
in real-world applications. In this paper, we propose a novel method DrugGPS
for generalizable structure-based drug design. With the biochemical priors, we
propose to learn subpocket prototypes and construct a global interaction graph
to model the interactions between subpocket prototypes and molecular motifs.
Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule
generation scheme are used to improve the model's performance. The experimental
results show that our model consistently outperforms baselines in generating
realistic drug candidates with high affinities in challenging
out-of-distribution settings.
