论文标题
分子设计的条件约束图形自动编码器
Conditional Constrained Graph Variational Autoencoders for Molecule Design
论文作者
论文摘要
近年来，图形的深层生成模型已用于生成新的分子。这些模型产生了良好的结果，导致文献中的几项建议。但是，这些模型可能会遇到麻烦学习有关化学世界的一些复杂法律。在这项工作中，我们探讨了原子价直方图在此类模型中推动分子产生的用法。我们介绍了有条件的限制图形自动编码器（CCGVAE），该模型在最先进的模型中实现了此键IDEA，并在两个通常采用的分子生成数据集上的几个评估指标上显示了改进的结果。
In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these models may have troubles learning some of the complex laws governing the chemical world. In this work, we explore the usage of the histogram of atom valences to drive the generation of molecules in such models. We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model, and shows improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.
