论文标题
使用结构基序的分子图分层生成
Hierarchical Generation of Molecular Graphs using Structural Motifs
论文作者
论文摘要
越来越多地采用了图形技术来发现药物。以前的图生成方法利用了相对较小的分子构建块，例如原子或简单的周期，将其有效性限制为较小的分子。确实，正如我们所证明的那样，它们的性能显着降低了较大的分子。在本文中，我们提出了一个新的层次图编码器，该编码器将其用作基本的构建块，它采用了更大，更灵活的图图主题。我们的编码器以一种从原子到连接的基序以精细到蛋白的方式为每个分子产生多分辨率表示。每个级别将下面的成分的编码与该级别的图表集成在一起。我们的自回归粗到最新解码器一次添加一个基序，并通过解决新图案的过程来解决其附着在新兴分子上的决定。我们对包括聚合物在内的多个分子生成任务进行评估，并表明我们的模型明显优于先前的最新基准。
Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. Each level integrates the encoding of constituents below with the graph at that level. Our autoregressive coarse-to-fine decoder adds one motif at a time, interleaving the decision of selecting a new motif with the process of resolving its attachments to the emerging molecule. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines.
