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Automated benchmarking of combined protein structure and ligand conformation prediction

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jcc.21334openalex-snapshot

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

inferredcites →https://doi.org/10.1002/prot.25431openalex-snapshot

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

inferredcites →https://doi.org/10.1002/prot.25815openalex-snapshot

Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO)

inferredcites →https://doi.org/10.1002/prot.26213openalex-snapshot

Continuous Automated Model EvaluatiOn (CAMEO)—Perspectives on the future of fully automated evaluation of structure prediction methods

inferredcites →https://doi.org/10.1002/prot.26515openalex-snapshot

New prediction categories in CASP15

inferredcites →https://doi.org/10.1002/prot.26562openalex-snapshot

Improved multimer prediction using massive sampling with AlphaFold in CASP15

inferredcites →https://doi.org/10.1002/prot.26601openalex-snapshot

Assessment of protein–ligand complexes in CASP15

inferredcites →https://doi.org/10.1006/jmbi.1998.2401openalex-snapshot

Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation 1 1Edited by J. Thornton

inferredcites →https://doi.org/10.1007/s10822-016-9946-8openalex-snapshot

D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions

inferredcites →https://doi.org/10.1007/s10822-017-0088-4openalex-snapshot

D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

inferredcites →https://doi.org/10.1007/s10822-018-0180-4openalex-snapshot

D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings

inferredcites →https://doi.org/10.1007/s10822-020-00289-yopenalex-snapshot

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

inferredcites →https://doi.org/10.1016/j.drudis.2012.06.011openalex-snapshot

Essential considerations for using protein–ligand structures in drug discovery

inferredcites →https://doi.org/10.1016/j.isci.2022.105920openalex-snapshot

How good are AlphaFold models for docking-based virtual screening?

inferredcites →https://doi.org/10.1016/j.jmb.2020.11.003openalex-snapshot

RCSB Protein Data Bank: Architectural Advances Towards Integrated Searching and Efficient Access to Macromolecular Structure Data from the PDB Archive

inferredcites →https://doi.org/10.1016/j.jmgm.2012.07.007openalex-snapshot

Teach–Discover–Treat (TDT): Collaborative computational drug discovery for neglected diseases

inferredcites →https://doi.org/10.1016/j.sbi.2023.102645openalex-snapshot

Modeling conformational states of proteins with AlphaFold

inferredcites →https://doi.org/10.1016/j.str.2011.08.006openalex-snapshot

A New Generation of Crystallographic Validation Tools for the Protein Data Bank

inferredcites →https://doi.org/10.1016/j.str.2017.10.009openalex-snapshot

Validation of Structures in the Protein Data Bank

inferredcites →https://doi.org/10.1016/j.str.2019.05.012openalex-snapshot

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

inferredcites →https://doi.org/10.1021/acs.jcim.1c00203openalex-snapshot

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

inferredcites →https://doi.org/10.1021/acs.jcim.5b00387openalex-snapshot

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge

inferredcites →https://doi.org/10.1021/acs.jcim.5b00523openalex-snapshot

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

inferredcites →https://doi.org/10.1021/acs.jcim.5b00654openalex-snapshot

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

inferredcites →https://doi.org/10.1021/acs.jpcb.2c04346openalex-snapshot

AlphaFold, Artificial Intelligence (AI), and Allostery

inferredcites →https://doi.org/10.1021/ci300604zopenalex-snapshot

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

inferredcites →https://doi.org/10.1021/ci9003943openalex-snapshot

Recipes for the Selection of Experimental Protein Conformations for Virtual Screening

inferredcites →https://doi.org/10.1021/jm0300330openalex-snapshot

Information Decay in Molecular Docking Screens against Holo, Apo, and Modeled Conformations of Enzymes

inferredcites →https://doi.org/10.1038/nbt.3820openalex-snapshot

Nextflow enables reproducible computational workflows

inferredcites →https://doi.org/10.1038/s41467-018-04964-5openalex-snapshot

Clustering huge protein sequence sets in linear time

inferredcites →https://doi.org/10.1038/s41586-021-03819-2openalex-snapshot

Highly accurate protein structure prediction with AlphaFold

inferredcites →https://doi.org/10.1038/s41592-020-0731-1openalex-snapshot

Measurement of atom resolvability in cryo-EM maps with Q-scores

inferredcites →https://doi.org/10.1093/bioinformatics/btac202openalex-snapshot

Impact of protein conformational diversity on AlphaFold predictions

inferredcites →https://doi.org/10.1093/bioinformatics/btu789openalex-snapshot

The chemical component dictionary: complete descriptions of constituent molecules in experimentally determined 3D macromolecules in the Protein Data Bank

inferredcites →https://doi.org/10.1093/database/bat031openalex-snapshot

The Protein Model Portal—a comprehensive resource for protein structure and model information

inferredcites →https://doi.org/10.1093/nar/28.1.235openalex-snapshot

The Protein Data Bank

inferredcites →https://doi.org/10.1093/nar/gks966openalex-snapshot

BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions

inferredcites →https://doi.org/10.1093/nar/gky949openalex-snapshot

Protein Data Bank: the single global archive for 3D macromolecular structure data

inferredcites →https://doi.org/10.1107/s0907444911050359openalex-snapshot

Implementing an X-ray validation pipeline for the Protein Data Bank

inferredcites →https://doi.org/10.1107/s0907444913007051openalex-snapshot

OpenStructure: an integrated software framework for computational structural biology

inferredcites →https://doi.org/10.1107/s2059798318002541openalex-snapshot

Validation of ligands in macromolecular structures determined by X-ray crystallography

inferredcites →https://doi.org/10.1107/s2059798322011901openalex-snapshot

Errors in structural biology are not the exception

inferredcites →https://doi.org/10.1186/s13321-018-0285-8openalex-snapshot

P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

inferredcites →https://doi.org/10.1186/s13321-021-00522-2openalex-snapshot

GNINA 1.0: molecular docking with deep learning

inferredcites →https://doi.org/10.12688/f1000research.20559.1openalex-snapshot

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

inferredcites →https://doi.org/10.1371/journal.pone.0177459openalex-snapshot

Singularity: Scientific containers for mobility of compute

inferredcites →https://doi.org/10.20982/tqmp.16.5.s003openalex-snapshot

Introduction to Anaconda and Python: Installation and setup

inferredcites →https://doi.org/10.3389/fbinf.2022.959160openalex-snapshot

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

inferredcites →https://doi.org/10.48550/arxiv.2202.05146openalex-snapshot

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

inferredcites →https://doi.org/10.48550/arxiv.2210.01776openalex-snapshot

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

inferredcites →https://doi.org/10.52202/068431-0525openalex-snapshot

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

inferredcites →https://doi.org/10.5281/zenodo.6034087openalex-snapshot

Data used for training in "EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction"

inferredcites →https://doi.org/10.7554/elife.89386openalex-snapshot

How accurately can one predict drug binding modes using AlphaFold models?

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/prot.70060openalex-snapshot

Beyond Single Chains: Benchmarking Macromolecular Complex Prediction Methods With the Continuous Automated Model EvaluatiOn ( CAMEO )

inferred← citeshttps://doi.org/10.1007/s11427-025-3147-2openalex-snapshot

Protein foundation models: a comprehensive survey

inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02777openalex-snapshot

ArtiDock: Accurate Machine Learning Approach to Protein–Ligand Docking Optimized for High-Throughput Virtual Screening

inferred← citeshttps://doi.org/10.1021/acscatal.3c02743openalex-snapshot

Machine Learning-Guided Protein Engineering

inferred← citeshttps://doi.org/10.1038/s41401-025-01721-5openalex-snapshot

Benchmarking co-folding methods to predict the structures of covalent protein–ligand complexes

inferred← citeshttps://doi.org/10.1038/s41467-024-51771-2openalex-snapshot

Modelling protein complexes with crosslinking mass spectrometry and deep learning

inferred← citeshttps://doi.org/10.1038/s41592-025-02973-zopenalex-snapshot

A fully automated benchmarking suite to compare macromolecular complexes

inferred← citeshttps://doi.org/10.1038/s41594-026-01797-5openalex-snapshot

Evaluating generalization in protein–ligand cofolding methods

inferred← citeshttps://doi.org/10.1039/d5sc02055gopenalex-snapshot

Unifying sequence-structure coding for advanced protein engineering via a multimodal diffusion transformer

inferred← citeshttps://doi.org/10.1080/17460441.2024.2349169openalex-snapshot

Recent advances in computational and experimental protein-ligand affinity determination techniques

inferred← citeshttps://doi.org/10.1093/bib/bbag206openalex-snapshot

Comprehensive evaluation of artificial intelligence-empowered approaches for protein–aptamer complex prediction

inferred← citeshttps://doi.org/10.1093/nar/gkaf336openalex-snapshot

Prediction and quality assessment of protein quaternary structure models using the MultiFOLD2 and ModFOLDdock2 servers

inferred← citeshttps://doi.org/10.1093/protein/gzag021openalex-snapshot

Assessing structural prediction accuracy for nanobody–small molecule complexes

inferred← citeshttps://doi.org/10.1101/2023.06.07.544059openalex-snapshot

Modelling protein complexes with crosslinking mass spectrometry and deep learning

inferred← citeshttps://doi.org/10.1101/2024.03.06.583325openalex-snapshot

IMPROVING ALPHAFOLD2 PERFORMANCE WITH A GLOBAL METAGENOMIC & BIOLOGICAL DATA SUPPLY CHAIN

inferred← citeshttps://doi.org/10.1101/2024.07.17.603955openalex-snapshot

PLINDER: The protein-ligand interactions dataset and evaluation resource

inferred← citeshttps://doi.org/10.1101/2025.02.03.636309openalex-snapshot

Have protein-ligand cofolding methods moved beyond memorisation?

inferred← citeshttps://doi.org/10.1101/2025.10.01.679833openalex-snapshot

GCP-VQVAE: A Geometry-Complete Language for Protein 3D Structure

inferred← citeshttps://doi.org/10.20944/preprints202404.0576.v1openalex-snapshot

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

inferred← citeshttps://doi.org/10.3389/frbis.2026.1937302openalex-snapshot

Benchmarking protein-ligand co-folding across Nav, Cav and Kv-family channels

inferred← citeshttps://doi.org/10.3390/ijms25158426openalex-snapshot

AI-Driven Deep Learning Techniques in Protein Structure Prediction

inferred← citeshttps://doi.org/10.7554/elife.91512openalex-snapshot

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning

inferred← citeshttps://doi.org/10.7554/elife.91512.4openalex-snapshot

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning

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