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EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery

https://doi.org/10.1002/pro.4065
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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/jcc.540130412openalex-snapshot
Automated docking with grid‐based energy evaluation
inferredcites →https://doi.org/10.1002/pro.4065openalex-snapshot
EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery
inferredcites →https://doi.org/10.1002/pro.5560070411openalex-snapshot
Flexible ligand docking using conformational ensembles
inferredcites →https://doi.org/10.1002/prot.10028openalex-snapshot
Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock
inferredcites →https://doi.org/10.1002/prot.20688openalex-snapshot
Molecular docking of balanol to dynamics snapshots of protein kinase A
inferredcites →https://doi.org/10.1002/prot.25899openalex-snapshot
Using machine learning to improve ensemble docking for drug discovery
inferredcites →https://doi.org/10.1006/jmbi.1996.0776openalex-snapshot
Molecular docking to ensembles of protein structures 1 1Edited by B. Honig
inferredcites →https://doi.org/10.1007/978-1-4939-1465-4_20openalex-snapshot
Molecular Docking to Flexible Targets
inferredcites →https://doi.org/10.1016/j.sbi.2008.01.004openalex-snapshot
Flexible ligand docking to multiple receptor conformations: a practical alternative
inferredcites →https://doi.org/10.1016/s0006-3495(02)73908-8openalex-snapshot
Bridging Implicit and Explicit Solvent Approaches for Membrane Electrostatics
inferredcites →https://doi.org/10.1021/acs.jcim.5b00299openalex-snapshot
Inexpensive Method for Selecting Receptor Structures for Virtual Screening
inferredcites →https://doi.org/10.1021/acs.jcim.5b00559openalex-snapshot
ZINC 15 – Ligand Discovery for Everyone
inferredcites →https://doi.org/10.1021/ar00041a001openalex-snapshot
Structure-Based Molecular Design
inferredcites →https://doi.org/10.1021/ci049714+openalex-snapshot
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
inferredcites →https://doi.org/10.1021/ci050412xopenalex-snapshot
Ensemble Docking into Flexible Active Sites. Critical Evaluation of FlexE against JNK-3 and β-Secretase
inferredcites →https://doi.org/10.1021/ci2003889openalex-snapshot
NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
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/ja0260162openalex-snapshot
Computational Drug Design Accommodating Receptor Flexibility: The Relaxed Complex Scheme
inferredcites →https://doi.org/10.1021/ja042260copenalex-snapshot
Representing Receptor Flexibility in Ligand Docking through Relevant Normal Modes
inferredcites →https://doi.org/10.1021/ja202726yopenalex-snapshot
How Does a Drug Molecule Find Its Target Binding Site?
inferredcites →https://doi.org/10.1021/jm049092jopenalex-snapshot
Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
inferredcites →https://doi.org/10.1021/jm300687eopenalex-snapshot
Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
inferredcites →https://doi.org/10.1021/jp3003992openalex-snapshot
Exploring Protein Flexibility: Incorporating Structural Ensembles From Crystal Structures and Simulation into Virtual Screening Protocols
inferredcites →https://doi.org/10.1021/jp506511popenalex-snapshot
Multi-Conformer Ensemble Docking to Difficult Protein Targets
inferredcites →https://doi.org/10.1038/ncomms7155openalex-snapshot
The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning
inferredcites →https://doi.org/10.1039/c4cp05440gopenalex-snapshot
Importance of protein flexibility in ranking inhibitor affinities: modeling the binding mechanisms of piperidine carboxamides as Type I1/2 ALK inhibitors
inferredcites →https://doi.org/10.1093/nar/28.1.235openalex-snapshot
The Protein Data Bank
inferredcites →https://doi.org/10.1093/protein/6.7.723openalex-snapshot
Matching chemistry and shape in molecular docking
inferredcites →https://doi.org/10.1111/j.1747-0285.2010.00996.xopenalex-snapshot
Derivatives of Salicylic Acid as Inhibitors of YopH in Yersinia pestis
inferredcites →https://doi.org/10.1142/s0219633619200013openalex-snapshot
Improving ensemble docking for drug discovery by machine learning
inferredcites →https://doi.org/10.1148/radiology.143.1.7063747openalex-snapshot
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1148/radiology.148.3.6878708openalex-snapshot
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
inferredcites →https://doi.org/10.1186/1758-2946-3-33openalex-snapshot
Open Babel: An open chemical toolbox
inferredcites →https://doi.org/10.1371/journal.pone.0018845openalex-snapshot
Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening
inferredcites →https://doi.org/10.1517/17460441.2015.1078308openalex-snapshot
Flexible receptor docking for drug discovery
inferredcites →https://doi.org/10.2174/156802610790232279openalex-snapshot
Emerging Methods for Ensemble-Based Virtual Screening
inferredcites →https://doi.org/10.3390/ijms20184331openalex-snapshot
Molecular Docking: Shifting Paradigms in Drug Discovery
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9783527830497.ch9openalex-snapshot
Mining for Bioactive Molecules in Open Databases
inferred← citeshttps://doi.org/10.1007/978-3-031-76718-0_9openalex-snapshot
Conformational Sampling of Proteins: Methods for Simulate Protein Plasticity and Ensemble Docking
inferred← citeshttps://doi.org/10.1007/978-981-95-2525-6_42openalex-snapshot
Integration of Artificial Intelligence in Molecular Docking and Virtual Screening Methods
inferred← citeshttps://doi.org/10.1016/bs.armc.2022.08.006openalex-snapshot
No dance, no partner! A tale of receptor flexibility in docking and virtual screening
inferred← citeshttps://doi.org/10.1016/j.biotechadv.2026.108803openalex-snapshot
Decoding polyphenol–protein interactions with deep learning: From molecular mechanisms to food applications
inferred← citeshttps://doi.org/10.1016/j.heliyon.2025.e41714openalex-snapshot
Computational epitope-based vaccine design with bioinformatics approach; a review
inferred← citeshttps://doi.org/10.1016/j.jmb.2025.169163openalex-snapshot
DINC-ensemble: A web server for docking large ligands incrementally to an ensemble of receptor conformations
inferred← citeshttps://doi.org/10.1021/acs.biochem.3c00165openalex-snapshot
Computational Exploration of Potential CFTR Binding Sites for Type I Corrector Drugs
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.3c01161openalex-snapshot
Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence
inferred← citeshttps://doi.org/10.1039/9781837070206-00583openalex-snapshot
e-Resources Relevant to Machine Learning Tools for Medicinal Chemistry
inferred← citeshttps://doi.org/10.1039/9781837070206-00721openalex-snapshot
Leveraging Machine Learning for Network Pharmacology
inferred← citeshttps://doi.org/10.3390/molecules29133158openalex-snapshot
Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides–Triazole Hybrids with Anticancer Activity
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