Structure-aware conditional variational auto-encoder for constrained molecule optimization

The goal of molecule optimization is to optimize molecular properties by modifying molecule structures. Conditional generative models provide a promising way to transfer the input molecules to the ones with better property. However, molecular properties are highly sensitive to small changes in molecular structures. This leads to an interesting thought that we can improve the property of molecules with limited modification in structure. In this paper, we propose a structure-aware conditional Variational Auto-Encoder, namely SCVAE, which exploits the topology of molecules as structure condition and optimizes the molecular properties with constrained structural modification. SCVAE leverages graph alignment of two-level molecule structures in an unsupervised manner to bind the structure conditions between two molecules. Then, this structure condition facilitates the molecule optimization with limited structural modification, namely, constrained molecule optimization, under a novel variational auto-encoder framework. Extensive experimental evaluations demonstrate that structure-aware CVAE generates new molecules with high similarity to the original ones and better molecular properties.