2022/12/11 by Masatsugu Yamada, Yamada, Masatsugu, Mahito Sugiyama +1
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Artificial Intelligence (cs.AI) #Biomolecules (q-bio.BM) #Chemical Synthesis and Analysis #Click Chemistry and Applications #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2302.00587
openalex publication_date 2022/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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.