  Designing molecules that bind to specific target proteins is a fundamental
task in drug discovery. Recent models leverage geometric constraints to
generate ligand molecules that bind cohesively with specific protein pockets.
However, these models cannot effectively generate 3D molecules with 2D skeletal
curtailments and property constraints, which are pivotal to drug potency and
development. To tackle this challenge, we propose GraphVF, a variational
flow-based framework that combines 2D topology and 3D geometry, for
controllable generation of binding 3D molecules. Empirically, our method
achieves state-of-the-art binding affinity and realistic sub-structural layouts
for protein-specific generation. In particular, GraphVF represents the first
controllable geometry-aware, protein-specific molecule generation method, which
can generate binding 3D molecules with tailored sub-structures and
physio-chemical properties. Our code is available at
https://github.com/Franco-Solis/GraphVF-code.
