Antibody function depends on a distribution of structural conformations, with different states exhibiting distinct binding properties. Accurately characterizing these ensembles is crucial for therapeutic design; however, current experimental data are sparse and noisy, and molecular simulations are computationally expensive. This frames antibody dynamics as a posterior inference and sampling problem: efficiently exploring high-dimensional conformational space and estimating probable structures with quantified uncertainty. Solving this problem would transform antibody design, advance machine learning for high-dimensional, multimodal inference, and bridge theory and experiment with rigorous structural confidence estimates.
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