用压缩拓扑特征提升分子GNN的准确率与可解释性
Topological Feature Compression for Molecular Graph Neural Networks
- 将高阶拓扑信号压缩后融入分子图网络
- 在多数基准上达到最高精度和鲁棒性
- 适合需要高效且可解释分子建模的研究者
分子表征学习的进展已为众多化学生物信息任务提供高效编码。然而，在保持预测精度、可解释性和计算效率之间取得平衡，仍是重大挑战。本文提出一种新型图神经网络架构，将压缩的高阶拓扑信号与标准分子特征结合，既捕捉全局几何信息，又保持计算可行性与人类可读结构。我们在从小分子数据集到复杂材料数据集的多个基准上评估模型，证明其在参数高效架构下表现优异，在几乎所有基准中均取得最佳性能。所有代码与结果已开源。
原文摘要 · Abstract (English)
Recent advances in molecular representation learning have produced highly effective encodings of molecules for numerous cheminformatics and bioinformatics tasks. However, extracting general chemical insight while balancing predictive accuracy, interpretability, and computational efficiency remains a major challenge. In this work, we introduce a novel Graph Neural Network (GNN) architecture that combines compressed higher-order topological signals with standard molecular features. Our approach captures global geometric information while preserving computational tractability and human-interpretable structure. We evaluate our model across a range of benchmarks, from small-molecule datasets to complex material datasets, and demonstrate superior performance using a parameter-efficient architecture. We achieve the best performing results in both accuracy and robustness across almost all benchmarks. We open source all code \footnote{All code and results can be found on Github https://github.com/rahulkhorana/TFC-PACT-Net}.
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