  Recently, deep generative models for molecular graphs are gaining more and
more attention in the field of de novo drug design. A variety of models have
been developed to generate topological structures of drug-like molecules, but
explorations in generating three-dimensional structures are still limited.
Existing methods have either focused on low molecular weight compounds without
considering drug-likeness or generate 3D structures indirectly using atom
density maps. In this work, we introduce Ligand Neural Network (L-Net), a novel
graph generative model for designing drug-like molecules with high-quality 3D
structures. L-Net directly outputs the topological and 3D structure of
molecules (including hydrogen atoms), without the need for additional atom
placement or bond order inference algorithm. The architecture of L-Net is
specifically optimized for drug-like molecules, and a set of metrics is
assembled to comprehensively evaluate its performance. The results show that
L-Net is capable of generating chemically correct, conformationally valid, and
highly druglike molecules. Finally, to demonstrate its potential in
structure-based molecular design, we combine L-Net with MCTS and test its
ability to generate potential inhibitors targeting ABL1 kinase.
