Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure

arXiv:2311.13466v2 Announce Type: replace-cross
Abstract: Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of graph neural networks (GNNs) with graph size as well as the relatively slow inference speeds inherent to diffusion models, many existing molecular diffusion models rely on coarse-grained representations of protein structure to make training and inference feasible. However, such coarse-grained representations discard …

abstract arxiv biology cs.lg design diffusion diffusion models drug design framework generative generative models gnns graph graph neural networks inference networks neural networks protein protein structure q-bio.bm scaling type