Molecular Graph Generation by Decomposition and Reassembling

TL;DR

This paper introduces a novel, interpretable molecular generation method based on decomposing molecules into subgraphs and reassembling them guided by reinforcement learning, improving the quality of generated drug-like molecules.

Contribution

It presents a decomposition-and-reassembling approach that avoids optimization in hidden space, enhancing interpretability and effectiveness in molecular design.

Findings

Outperforms existing methods on penalized log P and drug-likeness metrics.

Generates valid intermediate molecules during the process.

Effective in discovering better drug-like molecules.

Abstract

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.