论文标题
通过2级VAE提高分子特性
Improving Molecule Properties Through 2-Stage VAE
论文作者
论文摘要
变性自动编码器（VAE）是一种流行的药物发现方法，并且有人提出了大量的建筑和管道来提高其性能。但是，当数据位于嵌入更高维的环境空间中的低维歧管上时，VAE模型本身会遭受不足的损失，例如较差的流形恢复，并且它们在每个应用中都表现出不同的表现。它在药物发现中的后果有些探索。在本文中，我们研究了如何通过通过2阶段的VAE改善歧管恢复来提高通过VAE产生的数据的相似性，在该数据中，第二阶段VAE在第一个阶段的潜在空间的潜在空间中进行了训练。我们使用Chembl数据集和聚合物数据集对我们的方法进行了实验评估。在两个数据集中，2级VAE方法能够从先前存在的方法中显着改善属性统计信息。
Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance. But the VAE model itself suffers from deficiencies such as poor manifold recovery when data lie on low-dimensional manifold embedded in higher dimensional ambient space and they manifest themselves in each applications differently. The consequences of it in drug discovery is somewhat under-explored. In this paper, we study how to improve the similarity of the data generated via VAE and the training dataset by improving manifold recovery via a 2-stage VAE where the second stage VAE is trained on the latent space of the first one. We experimentally evaluated our approach using the ChEMBL dataset as well as a polymer datasets. In both dataset, the 2-stage VAE method is able to improve the property statistics significantly from a pre-existing method.
