MolRIME: Reciprocal Innovation-driven Multimodal Exchange for Molecular Representation Learning

Learning transferable molecular representations benefits from integrating complementary information from 2D chemical topology and 3D molecular geometry. Existing molecular pretraining methods exploit this complementarity through cross-modal alignment, knowledge transfer, or representation fusion. However, these approaches typically operate on modality representations as a whole, without distinguishing between information already captured by the other modality and information that has not yet been captured. This highlights a central challenge in cross-modal interaction: determining what information should actually be exchanged between modalities. To address this challenge, we propose Reciprocal Innovation-Driven Multimodal Exchange (RIME), which makes cross-modal communication conditional on what the receiving modality can already explain. RIME uses reciprocal cross-modal prediction to identify prediction residuals as cross-modal innovations, which are dynamically re-estimated and exchanged as the modality representations evolve. Building on RIME, we develop MolRIME, a molecular pretraining framework that realizes innovation-driven 2D–3D exchange through symmetry-preserving updates and combines it with geometric denoising and topology reconstruction. We evaluate MolRIME across molecular dynamics, quantum-chemical, physicochemical, and biological tasks. Controlled frozen-encoder comparisons further demonstrate the benefit of exchanging cross-modal innovations over complete modality representations, with MolRIME reducing test MAE by 6.1-8.5% on four of six evaluated QM9 properties using only 10% of the training labels.