论文标题
分子生成的脱离vae
Disentangle VAE for Molecular Generation
论文作者
论文摘要
自动分子生成在药物发现中起着重要作用，并且由于深度学习的成功使用，近年来受到了广泛关注。基于图的神经网络代表自动分子产生的最新方法。但是，生成具有所需特性的分子仍然具有挑战性，这是药物发现中的核心任务。在本文中，我们关注此任务，并提出一个可控的连接树变分自动编码器（C JTVAE），将提取器模块添加到VAE框架中以描述分子的某些特性。我们的方法能够在给定输入分子的情况下与所需特性产生类似的分子。实验结果令人鼓舞。
Automatic molecule generation plays an important role on drug discovery and has received a great deal of attention in recent years thanks to deep learning successful use. Graph-based neural network represents state of the art methods on automatic molecule generation. However, it is still challenging to generate molecule with desired properties, which is a core task in drug discovery. In this paper, we focus on this task and propose a Controllable Junction Tree Variational Autoencoder (C JTVAE), adding an extractor module into VAE framework to describe some properties of molecule. Our method is able to generate similar molecular with desired property given an input molecule. Experimental results is encouraging.
