ArticleBriefings in bioinformatics2026
Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- The cutting-edge advancements in biomaterials under the guidance of intelligence and bionics.Regenerative biomaterials · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanobodies are promising protein therapeutics due to their high-stability, low immunogenicity, and ease of production. However, experimental screening of high-affinity nanobodies and their post optimization remain costly and time-consuming due to the vast variant space. Here, we developed a computational approach that integrates graph neural networks (GNNs) with Monte Carlo Metropolis algorithm for nanobody design. We constructed a GNN model, AiPPA, to predict the protein-protein binding free energy (BFE) without requiring the complex structure, achieving a Pearson correlation of 0.62 on benchmark. We then combined AiPPA with Metropolis importance sampling to design low-BFE nanobodies from a non-affinity template. We applied this method to the antigen TL1A and generated two affinity nanobodies. This work establishes a physics-informed deep learning method for computational nanobody design, providing a novel development strategy for protein therapeutics.
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