分子图的分解与重组生成
Molecular Graph Generation by Decomposition and Reassembling
摘要：
设计具有所需化学性质的分子结构是药物发现和材料设计的基本任务。然而，由于分子候选空间的组合爆炸，寻找具有优化的所需性质的分子仍然是一项具有挑战性的任务。在这里，我们提出了一种新的\emph{decomposition-and-reassembling}为基础的方法，它不包括任何隐藏空间的优化，我们的生成过程是高度可解释的。我们的方法是一个两步的过程：在第一个分解步骤中，我们应用频繁子图挖掘的分子数据库收集较小的子图作为分子的构建块。在第二个重组步骤中，我们通过强化学习寻找所需的构建块，并将它们组合起来生成新的分子。实验结果表明，该方法不仅能够在惩罚$\log P$和药物相似性两个标准方面找到更好的分子，而且能够生成具有有效中间体的药物分子。
英文摘要：
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.
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