论文标题
可视化药物发现的深图生成模型
Visualizing Deep Graph Generative Models for Drug Discovery
论文作者
论文摘要
药物发现旨在设计具有临床试验特定所需特性的新型分子。在过去的几十年中，药物发现和开发一直是一个昂贵且耗时的过程。在大型化学数据和AI的驱动下，深层生成模型显示出加速药物发现过程的巨大潜力。现有作品研究了不同的深层生成框架的分子生成，但是，对可视化工具的关注较少，以快速演示和评估模型的结果。在这里，我们提出了一个可视化框架，该框架提供了交互式可视化工具，以可视化在深图生成模型的编码和解码过程中生成的分子，并提供实时分子优化功能。我们的工作试图通过一些视觉解释能力将黑匣子AI驱动的药物发现模型赋予能力。
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials. Over past decades, drug discovery and development have been a costly and time consuming process. Driven by big chemical data and AI, deep generative models show great potential to accelerate the drug discovery process. Existing works investigate different deep generative frameworks for molecular generation, however, less attention has been paid to the visualization tools to quickly demo and evaluate model's results. Here, we propose a visualization framework which provides interactive visualization tools to visualize molecules generated during the encoding and decoding process of deep graph generative models, and provide real time molecular optimization functionalities. Our work tries to empower black box AI driven drug discovery models with some visual interpretabilities.
