Molecular Graph Generation by Decomposition and Reassembling
This addresses the challenge of combinatorial explosion in molecular design for drug discovery and material design, representing an incremental improvement with interpretable generation.
The paper tackles the problem of generating molecular structures with desired chemical properties by proposing a decomposition-and-reassembling approach, which achieves better results in terms of penalized log P and drug-likeness criteria compared to standard methods.
Designing molecular structures with desired chemical properties is an essential task in drug discovery and material design. However, finding molecules with the optimized desired properties is still a challenging task due to combinatorial explosion of candidate space of molecules. Here we propose a novel \emph{decomposition-and-reassembling} based approach, which does not include any optimization in hidden space and our generation process is highly interpretable. Our method is a two-step procedure: In the first decomposition step, we apply frequent subgraph mining to a molecular database to collect smaller size of subgraphs as building blocks of molecules. In the second reassembling step, we search desirable building blocks guided via reinforcement learning and combine them to generate new molecules. Our experiments show that not only can our method find better molecules in terms of two standard criteria, the penalized $\log P$ and drug-likeness, but also generate drug molecules with showing the valid intermediate molecules.