Title: GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow. Abstract: Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent generative models leveraging geometrical constraints imposed by proteins and molecules  have shown great potential in generating protein-specific 3D molecules.  Nevertheless, these existing methods fail to generate 3D molecules with 2D skeletal curtailments, which encode pharmacophoric patterns essential to drug potency. To cope with this challenge, we propose GraphVF, which seamlessly integrates geometrical and skeletal restraints into a variational flow framework, where the former is captured through a flow transformation and the latter is encoded by an amortized factorized Gaussian. We empirically verify that our method achieves state-of-the-art performance on protein-specific 3D molecule generation in terms of binding affinity and some other drug properties. In particular, it represents the first controllable geometry-aware, protein-specific molecule generation method, which enables creating 3D molecules with specified chemical sub-structures or drug properties.
