Azam Shirali

BiMba: using Vision Mamba to predict protein sites that bind other proteins

A state-space–driven deep learning framework that leverages the efficient long-range modeling capability of the Vision Mamba architecture to learn from three-dimensional protein surfaces represented as two-dimensional geometric or physicochemical grids, establishing state-space models as efficient, interpretable, and scalable architectures for molecular surface learning.