  Structure-based drug design, i.e., finding molecules with high affinities to
the target protein pocket, is one of the most critical tasks in drug discovery.
Traditional solutions, like virtual screening, require exhaustively searching
on a large molecular database, which are inefficient and cannot return novel
molecules beyond the database. The pocket-based 3D molecular generation model,
i.e., directly generating a molecule with a 3D structure and binding position
in the pocket, is a new promising way to address this issue. Herein, we propose
VD-Gen, a novel pocket-based 3D molecular generation pipeline. VD-Gen consists
of several carefully designed stages to generate fine-grained 3D molecules with
binding positions in the pocket cavity end-to-end. Rather than directly
generating or sampling atoms with 3D positions in the pocket like in early
attempts, in VD-Gen, we first randomly initialize many virtual particles in the
pocket; then iteratively move these virtual particles, making the distribution
of virtual particles approximate the distribution of molecular atoms. After
virtual particles are stabilized in 3D space, we extract a 3D molecule from
them. Finally, we further refine atoms in the extracted molecule by iterative
movement again, to get a high-quality 3D molecule, and predict a confidence
score for it. Extensive experiment results on pocket-based molecular generation
demonstrate that VD-Gen can generate novel 3D molecules to fill the target
pocket cavity with high binding affinities, significantly outperforming
previous baselines.
