论文标题
药物设计的分子生成：图形学习视角
Molecule Generation for Drug Design: a Graph Learning Perspective
论文作者
论文摘要
机器学习，尤其是图形学习，对其在各个领域的变革性影响的认识越来越高。一种有希望的应用是分子设计和发现的领域，尤其是在制药行业中。我们的调查提供了分子设计中最新方法的全面概述，尤其是侧重于\ emph {de de novo}药物设计，其中包含了（深）图形学习技术。我们将这些方法分为三个不同的组：\ emph {i）} \ emph {all-at-once}，\ emph {ii）} \ emph {基于fragment {fragment}和\ emph {iii）} \ emph {node-by-node}。此外，我们介绍了一些关键的公共数据集，并概述了分子生成和优化的常用评估指标。最后，我们讨论了该领域的现有挑战，并提出了未来研究的潜在方向。
Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on \emph{de novo} drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: \emph{i)} \emph{all-at-once}, \emph{ii)} \emph{fragment-based}, and \emph{iii)} \emph{node-by-node}. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.
