Physical probes expose and alleviate chemical-environment collapse in molecular representations

TL;DR

This paper introduces CLAIM, a contrastive learning framework that aligns molecular topologies with NMR data to improve atom-level molecular representations and property predictions.

Contribution

It presents a novel spectral alignment approach that alleviates chemical-environment collapse in molecular representations, enhancing various molecular property tasks.

Findings

CLAIM improves atom-level molecule-spectrum retrieval accuracy.

It enhances stereoisomer discrimination without explicit 3D modeling.

CLAIM transfers effectively to broader molecular property prediction tasks.

Abstract

Nuclear magnetic resonance (NMR) spectroscopy provides an experimental readout of local chemical environments, but its use in molecular representation learning has been constrained by heterogeneous data and incomplete atom-level assignments. Here we construct complementary high-fidelity experimental and computational 13C NMR resources, which reveal a recurrent form of representational collapse: atoms that are equivalent in molecular topology can remain experimentally distinct in their real chemical environments, whereas explicit 3D descriptions are further limited by static conformations in dynamic regimes. To alleviate this bottleneck, we develop CLAIM (Contrastive Learning for Atom-to-molecule Inference of Molecular NMR), a framework that aligns efficient topological molecular inputs with atom-resolved NMR observables. Through hierarchical chemical priors and cross-level contrastive learning, CLAIM restores lost chemical resolution and markedly improves atom-level molecule-spectrum retrieval. CLAIM remains robust in flexible and tautomeric systems for 13C NMR prediction, improves stereoisomer discrimination without explicit 3D modelling, and transfers to broader molecular property tasks including ADMET prediction and fluorescence estimation. These results establish physically grounded spectral alignment as an effective strategy for alleviating chemical-environment collapse and for guiding experimentally grounded molecular representation learning.