ArticleNature communications2024
DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed.
- ThebioRxiv : the preprint server for biology · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Quantum Computing Calculations of Protein-Ligand Binding Energies Using Decomposition Methods on Simulated and Real Quantum Hardware.Journal of chemical information and modeling · 2026Article
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- P-glycoprotein-mediated efflux of rapaprotin-L dictates sensitivity of cancer cells to the inducer of 26S proteasome disassembly.European journal of pharmacology · 2026Article
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Cryptic pockets in proteins: Harnessing conformational dynamics for rational drug design.Acta pharmaceutica Sinica. B · 2026Article
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- Extracellular matrix and proteolysis: mechanisms driving irreversible changes and shaping cell behavior.The FEBS journal · 2026Review
- The LiPP Benchmark Set for Modeling Lipid-Protein Complexes: Comparison of Co-Folding and Docking Methods.Journal of chemical information and modeling · 2026Article
- Precision culturomics enabled by unlabeled single-cell morphology and Raman spectra.Nature communications · 2026Article
- Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1.bioRxiv : the preprint server for biology · 2026Article
- DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.Interdisciplinary sciences, computational life sciences · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- Reimagining pharmacology education.Pharmacological reviews · 2026Review
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.