{"ID":2921223,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-04T00:54:56.190393508Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01628","arxiv_id":"2606.01628","title":"Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling","abstract":"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.","short_abstract":"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, existi...","url_abs":"https://arxiv.org/abs/2606.01628","url_pdf":"https://arxiv.org/pdf/2606.01628v1","authors":"[\"Keyue Qiu\",\"Xintong Wang\",\"Zhilong Zhang\",\"Hao Zhou\",\"Wei-Ying Ma\"]","published":"2026-06-01T03:25:17Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}