针对分子属性回归中数据稀缺问题，SPECTRA通过频域感知生成提升预测效果。
SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
- 基于频谱感知与稀疏预算，聚焦低样本区域生成分子图
- 在关键属性区间性能超越主流方法，计算效率提升4倍
- 适合药物发现中罕见但重要的分子属性预测任务
分子属性回归在化学相关目标范围数据不足时表现不佳。标准平均误差最小化方法在此类重要情形下性能下降，而过采样会导致无意义的分子表示。本文提出SPECTRA，一种频谱域感知的图生成方法，旨在提升低频但关键的分子属性预测能力。该方法结合稀有性感知的生成预算机制、目标邻域图对齐以建立结构对应关系，以及拉普拉斯谱、节点特征和目标值的插值策略。配合使用边感知的切比雪夫卷积谱图神经网络，SPECTRA在多个属性预测基准上表现优异，在关键目标范围内超越现有领先方法，同时计算耗时减少约4倍。
原文摘要 · Abstract (English)
Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average error minimization approaches underperform in these highly relevant cases, and oversampling approaches lead to meaningless molecular representations. In this paper, we propose SPECTRA, a spectral, domain-aware graph generation method designed to improve the prediction of underrepresented but relevant molecular property values. It combines a rarity-aware budgeting scheme to focus generation where data are scarce, target-neighbors graph alignment to establish structural correspondence, and interpolation of Laplacian spectra, node features, and targets. Coupled with spectral GNN using edge-aware Chebyshev convolutions, SPECTRA shows its effectiveness in property prediction benchmarks with competitive performance over leading state-of-the-art methods in relevant target ranges, while requiring ~4x less computational time.
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