ArticleStructural dynamics (Melville, N.Y.)2024
ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.
Article in Structural dynamics (Melville, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- LINKER:Journal of chemical information and modeling · 2026Article
- Multimodal Learning of Protein-Protein Interactions for Accurate Binding Affinity Prediction.ACS omega · 2025Article
- Artificial intelligence in structural biology: Preface.Structural dynamics (Melville, N.Y.) · 2025Article
- Article
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an diffusion framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiff's potential to advance computational drug discovery and protein engineering.
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Registered trials
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