Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
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Abstract (arXiv)
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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References · 21
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- 2020ProGen: Language Modeling for Protein Generationcited by 127
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- 2024Diffusion Language Models Are Versatile Protein Learnerscited by 93
- 2024DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Designcited by 87
- 2023Bayesian Flow Networkscited by 62
- 2024DPLM-2: A Multimodal Diffusion Protein Language Modelcited by 59
- 2024MolCRAFT: Structure-Based Drug Design in Continuous Parameter Spacecited by 48
- 2025La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matchingcited by 26
- 2024ProteinBench: A Holistic Evaluation of Protein Foundation Modelscited by 23
- 2025Multi-domain Distribution Learning for De Novo Drug Designcited by 15
- 2025Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedulecited by 6
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