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arxiv: 2606.01628 · v1 · pith:DZR5G6VCnew · submitted 2026-06-01 · 🧬 q-bio.BM · cs.AI

Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling

classification 🧬 q-bio.BM cs.AI
keywords coupledcouplingbiomolecularbiomoleculesco-designcouplingsdesigngenerative
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Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structure. Recent generative models for biomolecular co-design aim to capture this interplay by jointly modeling coupled modalities. However, existing approaches largely adopt a parallel execution of marginal generative processes, implicitly enforcing fixed synchronous coupling. We argue that a critical but overlooked degree of freedom lies in how these marginal processes are temporally coupled during training and generation, where inappropriate coupling can introduce high-variance supervision and inconsistent intermediate states, affecting modality consistency. To address this, we introduce GeoCoupling, a systematic framework that optimizes for temporal couplings between heterogeneous modalities. Empirical results across structure-based drug design and unconditional protein design demonstrate the learned couplings consistently outperform synchronous and randomly coupled baselines, yielding biomolecules with improved physical validity and diversity.

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