ArticleNature methods2025
SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Data driven selection of consensus docking pipelines for structure based hit identification.npj drug discovery · 2026Article
- Design and synthesis of oxathiapiprolin derivatives containing acetal fragments as potent fungicide candidates.Pest management science · 2026Article
- Teaching diffusion models physics: reinforcement learning for physically valid diffusion-based docking.Chemical science · 2026Article
- EasyDock 1.3: An Automated Pipeline for Molecular Docking.Journal of chemical information and modeling · 2026Article
- Atomic-level protein-ligand recognition with PBCNet2.0 for probe discovery.Nature chemical biology · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.Journal of cheminformatics · 2026Article
- Tightly coupled equivariant flow matching for molecular docking with multimodal physical constraints.Molecular diversity · 2026Article
- A general strategy for high-efficiency live bacteria imaging and targeted phototherapy.Chemical science · 2026Article
- 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
- The LiPP Benchmark Set for Modeling Lipid-Protein Complexes: Comparison of Co-Folding and Docking Methods.Journal of chemical information and modeling · 2026Article
- ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions.bioRxiv : the preprint server for biology · 2026Article
- PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.Journal of cheminformatics · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- AI-enabled protein design facilitates future plant research and crop breeding.Plant physiology · 2026Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, optimizing molecular translations, rotations and torsions to generate reliable binding poses. Our extensive evaluations across various benchmarks demonstrate SurfDock's superiority over existing methods in docking success rates and adherence to physical constraints. It also exhibits remarkable generalizability to unseen proteins and predicted apo structures, while achieving state-of-the-art performance in virtual screening tasks. In a real-world application, SurfDock identified seven novel hit molecules in a virtual screening project targeting aldehyde dehydrogenase 1B1, a key enzyme in cellular metabolism. This showcases SurfDock's ability to elucidate molecular mechanisms underlying cellular processes. These results highlight SurfDock's potential as a transformative tool in structural biology, offering enhanced accuracy, physical plausibility and practical applicability in understanding protein-ligand interactions.
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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.