  Finding drug-like compounds with high bioactivity is essential for drug
discovery, but the task is complicated by the high cost of chemical synthesis
and validation. With their outstanding performance in de novo drug design, deep
generative models represent promising tools for tackling this challenge. In
recently years, 3D molecule generative models have gained increasing attention
due to their ability to directly utilize the 3D interaction information between
the target and ligand. However, it remains challenging to synthesize the
molecules generated by these models, limiting the speed of bioactivity
validation and further structure optimization. In this work, we propose
DeepLigBuilder+, a deep generative model for 3D molecules that combines
structure-based de novo drug design with a reaction-based generation framework.
Besides producing 3D molecular structures, the model also proposes synthetic
pathways for generated molecules, which greatly assists the retro-synthetic
analysis. To achieve this, we developed a new way to enforce the
synthesizability constraint using a tree-based organization of purchasable
building blocks. This method enjoys high scalability and is compatible with
existing atom-based generative models. Additionally, for structure-based design
tasks, we developed an SE(3)-equivariant transformer conditioned on the shape
and pharmacophore-based inputs, and combine it with the Monte Carlo tree
search. Using the ATP-binding pocket of BTK and the NAD+ binding pocket of
PHGDH for case studies, we demonstrate that DeepLigBuilder+ is capable of
enriching drug-like molecules with high predicted binding affinity and
desirable interaction modes while maintaining the synthesizability constraint.
We believe that DeepLigBuilder+ is a powerful tool for accelerating the process
of drug discovery.
