MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao Thomas Kipf
Abstract
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to $100\%$ valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse.
中文速览
化学家在寻找具有特定性质的新分子时，面临着巨大离散搜索空间的挑战，现有深度生成模型大多依赖字符串表示（SMILES）或需要昂贵的图匹配计算。MolGAN 直接在分子图（molecular graph）结构上操作，将生成对抗网络（GAN）与强化学习（RL）目标结合：GAN 负责让生成的分子在分布上接近真实化合物，RL 则引导生成过程朝向可溶性、可合成性等特定化学性质优化，同时借助图卷积构建置换不变的判别器，彻底绕开了节点排序和图匹配难题。在 QM9 化学数据库上的实验表明，MolGAN 能生成接近 100% 化学有效的分子，在多项指标上优于基于 SMILES 的方法和直接生成图的似然模型，尽管存在模式坍塌（mode collapse）的问题。这项工作为图结构数据的生成建模提供了一条高效的新路径，对药物发现等领域具有重要意义。
原文 arXiv:1805.11973;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1805.11973v2