Physical Probes Expose and Alleviate Chemical-Environment Collapse in Molecular Representations
Researchers have introduced CLAIM, a novel framework designed to address 'chemical-environment collapse' in molecular representation learning. While Nuclear Magnetic Resonance (NMR) spectroscopy offers valuable insights into local chemical environments, its application has been hindered by data heterogeneity and incomplete atom-level assignments. The study reveals that atoms topologically equivalent can remain experimentally distinct, a nuance often lost in static 3D descriptions. CLAIM utilizes contrastive learning to align efficient topological molecular inputs with atom-resolved NMR observables, leveraging hierarchical chemical priors. This approach successfully restores lost chemical resolution and significantly enhances atom-level molecule-spectrum retrieval. The framework demonstrates robustness in predicting 13C NMR for flexible and tautomeric systems, improves stereoisomer discrimination without explicit 3D modeling, and effectively transfers to broader tasks such as ADMET prediction and fluorescence estimation. These findings establish physically grounded spectral alignment as a critical strategy for improving experimentally grounded molecular representation learning, bridging the gap between computational models and experimental physical probes.
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Physical Probes Expose and Alleviate Chemical-Environment Collapse in Molecular Representations
Researchers have introduced CLAIM, a novel framework designed to address 'chemical-environment collapse' in molecular representation learning. While Nuclear Magnetic Resonance (NMR) spectroscopy offers valuable insights into local chemical environments, its application has been hindered by data heterogeneity and incomplete atom-level assignments. The study reveals that atoms topologically equivalent can remain experimentally distinct, a nuance often lost in static 3D descriptions. CLAIM utilizes contrastive learning to align efficient topological molecular inputs with atom-resolved NMR observables, leveraging hierarchical chemical priors. This approach successfully restores lost chemical resolution and significantly enhances atom-level molecule-spectrum retrieval. The framework demonstrates robustness in predicting 13C NMR for flexible and tautomeric systems, improves stereoisomer discrimination without explicit 3D modeling, and effectively transfers to broader tasks such as ADMET prediction and fluorescence estimation. These findings establish physically grounded spectral alignment as a critical strategy for improving experimentally grounded molecular representation learning, bridging the gap between computational models and experimental physical probes.
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