ArticleNature structural & molecular biology2026
Evaluating generalization in protein-ligand cofolding methods.
Article in Nature structural & molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Molecular dockingDigital discovery · 2026Article
- A curated benchmark for cofolding models on kinase conformational states.npj drug discovery · 2026Article
- Prioritizing Candidate YB‑1 Cold Shock Domain Ligands via 5D-ElectroShape and Boltz Generative Cofolding.ACS omega · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Teaching diffusion models physics: reinforcement learning for physically valid diffusion-based docking.Chemical science · 2026Article
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- Predicting Ligand Binding Modes by Scaffold-Guided Structure Refinement.Journal of medicinal chemistry · 2026Article
- Attracting Cavities 3.0: faster and more versatile molecular docking for the SwissDock webserver.Bioinformatics (Oxford, England) · 2026Article
- Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip.Communications chemistry · 2026Article
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- KinConfBench: A Curated Benchmark for Cofolding Models on Kinase Conformational States.bioRxiv : the preprint server for biology · 2026Article
- NanoporeDB: a structural resource of multimeric protein nanopores for single-molecule sensing.GigaScience · 2026Article
- Assessing structural prediction accuracy for nanobody-small molecule complexes.Protein engineering, design & selection : PEDS · 2026Article
- In Silico Analysis of Potential Stabilizer Binding Sites at Protein-RNA Interfaces.Computational and structural biotechnology journal · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.Proteins · 2026Article
- Polaris Challenge: Data-Driven Priors to Improve Docking for Pose Prediction.Journal of chemical information and modeling · 2025Article
- How many crystal structures do you need to trust your docking results?bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has driven major breakthroughs in protein structure prediction; however, one of the next critical steps forward is accurately predicting how proteins interact with small-molecule ligands, to enable real-world applications such as drug discovery. Recent cofolding methods aim to address this challenge, but evaluating their performance has been inconclusive because of the lack of relevant benchmarking datasets. Here we present a comprehensive evaluation of four leading all-atom cofolding methods using our newly introduced benchmark dataset, Runs N' Poses. Runs N' Poses comprises 2,600 high-resolution protein-ligand systems released after the training cutoff used by these methods. We demonstrate that current cofolding approaches largely memorize ligand poses from their training data, hindering their use for de novo drug design. With this assessment and benchmark dataset, we aim to accelerate progress in the field by allowing for a more realistic assessment of the current state-of-the-art deep learning methods for predicting 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.