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Multistep generative backmapping of coarse-grained structures
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DOI：10.1016/j.cpc.2026.110286.png)
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Abstract
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Data-driven backmapping from coarse-grained (CG) to fine-grained (FG) representations remains challenging for complex biomolecular systems such as proteins, where methods often suffer from limited accuracy, training instability, and compromised physical realism. We present a novel multistep generative framework that enables stepwise refinement from CG beads to full FG detail by integrating conditional Variational Autoencoders with graph-based neural networks. The probabilistic formulation of multistep backmapping is outlined, and numerical experiments on proteins with diverse structures and ultra-coarse representations demonstrate that multistep schemes substantially enhance reconstruction accuracy while improving computational efficiency during training compared to single-step alternatives.
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