  Generating molecules that bind to specific proteins is an important but
challenging task in drug discovery. Previous works usually generate atoms in an
auto-regressive way, where element types and 3D coordinates of atoms are
generated one by one. However, in real-world molecular systems, the
interactions among atoms in an entire molecule are global, leading to the
energy function pair-coupled among atoms. With such energy-based consideration,
the modeling of probability should be based on joint distributions, rather than
sequentially conditional ones. Thus, the unnatural sequentially auto-regressive
modeling of molecule generation is likely to violate the physical rules, thus
resulting in poor properties of the generated molecules. In this work, a
generative diffusion model for molecular 3D structures based on target proteins
as contextual constraints is established, at a full-atom level in a
non-autoregressive way. Given a designated 3D protein binding site, our model
learns the generative process that denoises both element types and 3D
coordinates of an entire molecule, with an equivariant network. Experimentally,
the proposed method shows competitive performance compared with prevailing
works in terms of high affinity with proteins and appropriate molecule sizes as
well as other drug properties such as drug-likeness of the generated molecules.
