arXiv:2602.24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data. Recent experiment-guided generators attempt to address this by steering the reverse diffusion process. However, these methods are limited by fixed sampling horizons and sensitivity to initialization, often yielding thermodynamically implausible results. We introduce a general inference-time optimization framework to solve these challenges. First, we optimize over latent
The field of generative AI for biology is rapidly maturing, and there's a growing need to align computational models more closely with real-world experimental data to unlock practical applications.
Improving the accuracy and experimental grounding of generative protein models could accelerate drug discovery, material science, and bioengineering, impacting multiple high-value industries.
The ability to generate thermodynamically plausible and experimentally validated protein ensembles at inference time shifts the paradigm from static protein structures to dynamic, functional ensembles for design.
- · Biopharmaceutical companies
- · Synthetic biology startups
- · AI-driven drug discovery platforms
- · Materials science R&D
- · Companies relying solely on traditional experimental protein characterization
- · Less accurate computational protein design methods
More accurate and reliable computational models for protein behavior.
Reduced timelines and costs for developing novel therapeutics and biomaterials.
The creation of entirely new protein functions and designer organisms for industrial or medical purposes.
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Read at arXiv cs.LG