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Designing molecules that bind to specific target proteins is a fundamentaltask in drug discovery. Recent models leverage geometric constraints togenerate ligand molecules that bind cohesively with specific protein pockets.However, these models cannot effectively generate 3D molecules with 2D skeletalcurtailments and property constraints, which are pivotal to drug potency anddevelopment. To tackle this challenge, we propose GraphVF, a variationalflow-based framework that combines 2D topology and 3D geometry, forcontrollable generation of binding 3D molecules. Empirically, our methodachieves state-of-the-art binding affinity and realistic sub-structural layoutsfor protein-specific generation. In particular, GraphVF represents the firstcontrollable geometry-aware, protein-specific molecule generation method, whichcan generate binding 3D molecules with tailored sub-structures andphysio-chemical properties. Our code is available athttps://github.com/Franco-Solis/GraphVF-code.

GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow