DecompOpt: Controllable and Decomposed Diffusion Models for
  Structure-based Molecular Optimization
Xiangxin Zhou Xiwei Cheng Yuwei Yang Yu Bao Liang Wang Quanquan Gu
Recently, 3D generative models have shown promising performances in
structure-based drug design by learning to generate ligands given target
binding sites. However, only modeling the target-ligand distribution can hardly
fulfill one of the main goals in drug discovery -- designing novel ligands with
desired properties, e.g., high binding affinity, easily synthesizable, etc.
This challenge becomes particularly pronounced when the target-ligand pairs
used for training do not align with these desired properties. Moreover, most
existing methods aim at solving \textit{de novo} design task, while many
generative scenarios requiring flexible controllability, such as R-group
optimization and scaffold hopping, have received little attention. In this
work, we propose DecompOpt, a structure-based molecular optimization method
based on a controllable and decomposed diffusion model. DecompOpt presents a
new generation paradigm which combines optimization with conditional diffusion
models to achieve desired properties while adhering to the molecular grammar.
Additionally, DecompOpt offers a unified framework covering both \textit{de
novo} design and controllable generation. To achieve so, ligands are decomposed
into substructures which allows fine-grained control and local optimization.
Experiments show that DecompOpt can efficiently generate molecules with
improved properties than strong de novo baselines, and demonstrate great
potential in controllable generation tasks.