AI Insight
SPECTRA is a graph generation framework designed to address the challenge of imbalanced molecular property regression, where chemically relevant target ranges are underrepresented in training datasets. The method combines a rarity-aware budgeting scheme, target-neighbors graph alignment, and interpolation of Laplacian spectra to generate meaningful synthetic molecular data in scarce regions. When coupled with a spectral graph neural network using edge-aware Chebyshev convolutions, SPECTRA achieves competitive performance on property prediction benchmarks while requiring approximately four times less computational time than leading methods.
Why it matters
Improving prediction accuracy in underrepresented but chemically relevant molecular property ranges has direct implications for drug discovery and materials science, where models must reliably identify candidates with specific target properties. A computationally efficient solution to this problem could accelerate virtual screening pipelines and reduce costs in early-stage research.
arXiv:2511.04838v2 Announce Type: replace-cross
Abstract: 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.
Source: SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression