用图模型理解分子蛋白，加速化学新发现
Graph Data Modeling: Molecules, Proteins, & Chemical Processes
- 把分子蛋白当作图结构，用图神经网络分析
- 可预测分子性质、反应路径等关键化学任务
- 适合从事药物研发与材料设计的研究者
图在化学科学中具有核心地位，自然地描述了分子、蛋白质、反应及工业过程。它们捕捉了决定材料、生物和医学特性的相互作用与结构。本文《图数据建模：分子、蛋白质与化学过程》介绍图作为化学中的数学对象，并展示学习算法（特别是图神经网络）如何在其上运行。文章概述了图设计的基础、关键预测任务、化学科学中的代表性应用，以及机器学习在基于图的建模中的作用。这些内容共同为读者应用图方法于下一代化学发现做好准备。
原文摘要 · Abstract (English)
Graphs are central to the chemical sciences, providing a natural language to describe molecules, proteins, reactions, and industrial processes. They capture interactions and structures that underpin materials, biology, and medicine. This primer, Graph Data Modeling: Molecules, Proteins, & Chemical Processes, introduces graphs as mathematical objects in chemistry and shows how learning algorithms (particularly graph neural networks) can operate on them. We outline the foundations of graph design, key prediction tasks, representative examples across chemical sciences, and the role of machine learning in graph-based modeling. Together, these concepts prepare readers to apply graph methods to the next generation of chemical discovery.
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