[2602.24007] Inference-time optimization for experiment-grounded protein ensemble generation

Quantitative Biology > Biomolecules arXiv:2602.24007 (q-bio) [Submitted on 27 Feb 2026] Title:Inference-time optimization for experiment-grounded protein ensemble generation Authors:Advaith Maddipatla, Anar Rzayev, Marco Pegoraro, Martin Pacesa, Paul Schanda, Ailie Marx, Sanketh Vedula, Alex M. Bronstein View a PDF of the paper titled Inference-time optimization for experiment-grounded protein ensemble generation, by Advaith Maddipatla and 7 other authors View PDF HTML (experimental) 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 representations to maximize ensemble log-likelihood, rather than perturbing structures post hoc. This approach eliminates dependence on diffusion length, removes initialization bias, and easily incorporates external constraints. Second, we present novel sampling schemes for drawing Boltzmann-weighted ensembles. By combining structural priors from AlphaFold3 with force-field-based priors, we sample from their product distribution while balancing experime...