SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
arXiv:2511.04838v2 Announce Type: replace 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
The paper addresses a long-standing challenge in molecular property prediction by proposing a novel graph generation method, indicating continuous advancements in AI for scientific discovery.
Improving molecular property regression, especially for underrepresented cases, accelerates drug discovery, materials science, and overall synthetic biology applications.
The ability to generate meaningful molecular representations for rare but important property values could lead to more robust and reliable AI-driven molecular design processes.
- · Pharmaceutical companies
- · Materials science
- · AI for drug discovery platforms
- · Synthetic biology researchers
- · Traditional drug screening methods
- · Trial-and-error chemistry approaches
More efficient identification of molecular candidates with desired, hard-to-predict properties.
Reduced R&D costs and accelerated time-to-market for new drugs and materials.
Enhanced global competitiveness for nations with strong AI and synthetic biology research capabilities, potentially leading to new economic sectors.
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Read at arXiv cs.LG