提出可解释四层级分子结构的图神经网络，助力药物与材料研发。
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability
- 基于图神经网络，从原子到片段连接四层解析分子贡献
- 能识别关键原子、键、片段及片段间非标准连接的重要性
- 适合需要科学洞察的药物设计与新材料开发场景
分子性质预测在药物研发和储能材料设计等前沿领域至关重要。尽管已有多种机器学习模型用于此任务，但兼具高精度与可解释性的模型仍属少数。本文提出一种图神经网络，不仅达到领先模型的预测精度，还能在四个层面提供可解释性：原子、化学键、分子片段以及片段间的连接关系。该模型特别适用于分析非传统键连接的分子子结构，能够量化特定片段对性质预测的影响，从而识别出提升或降低目标性质的关键片段。这些可解释特征有助于从模型学习到的结构-性质关联中提取科学洞见。
原文摘要 · Abstract (English)
Molecular property prediction is essential in a variety of contemporary scientific fields, such as drug development and designing energy storage materials. Although there are many machine learning models available for this purpose, those that achieve high accuracy while also offering interpretability of predictions are uncommon. We present a graph neural network that not only matches the prediction accuracies of leading models but also provides insights on four levels of molecular substructures. This model helps identify which atoms, bonds, molecular fragments, and connections between fragments are significant for predicting a specific molecular property. Understanding the importance of connections between fragments is particularly valuable for molecules with substructures that do not connect through standard bonds. The model additionally can quantify the impact of specific fragments on the prediction, allowing the identification of fragments that may improve or degrade a property value. These interpretable features are essential for deriving scientific insights from the model's learned relationships between molecular structures and properties.
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