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Cross‐docking benchmark for automated pose and ranking prediction of ligand binding
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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.20893openalex-snapshot
Assessment of programs for ligand binding affinity prediction
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.20512openalex-snapshot
Binding MOAD (Mother Of All Databases)
inferredcites →https://doi.org/10.1007/s10822-008-9196-5openalex-snapshot
Recommendations for evaluation of computational methods
inferredcites →https://doi.org/10.1007/s10822-016-9941-0openalex-snapshot
Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge
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/s10822-017-0065-yopenalex-snapshot
Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges
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/10.1016/j.cbpa.2018.06.006openalex-snapshot
Improving small molecule virtual screening strategies for the next generation of therapeutics
inferredcites →https://doi.org/10.1021/acs.jcim.5b00338openalex-snapshot
Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment
inferredcites →https://doi.org/10.1021/ci200082topenalex-snapshot
CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes
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/ci4000486openalex-snapshot
CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys
inferredcites →https://doi.org/10.1021/ci8002254openalex-snapshot
Protein−Ligand Docking against Non-Native Protein Conformers
inferredcites →https://doi.org/10.1021/jm030580lopenalex-snapshot
The PDBbind Database: Collection of Binding Affinities for Protein−Ligand Complexes with Known Three-Dimensional Structures
inferredcites →https://doi.org/10.1021/jm048957qopenalex-snapshot
The PDBbind Database: Methodologies and Updates
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/10.1093/bioinformatics/btr550openalex-snapshot
MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories
inferredcites →https://doi.org/10.1093/nar/gkl999openalex-snapshot
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
inferredcites →https://doi.org/10.1093/nar/gku1088openalex-snapshot
Recent improvements to Binding MOAD: a resource for protein–ligand binding affinities and structures
inferredcites →https://doi.org/10.1186/1471-2105-10-168openalex-snapshot
Fpocket: An open source platform for ligand pocket detection
inferredcites →https://doi.org/10.1186/1758-2946-3-33openalex-snapshot
Open Babel: An open chemical toolbox
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Open Access Databases and Datasets for Computer‐Aided Drug Design. A Short List Used in the Molecular Modelling Group of the SIB
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Community Benchmarking Exercises for Docking and Scoring
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CIFDock : A novel CHARMM ‐based flexible receptor–flexible ligand docking protocol
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Improving ligand‐ranking of AutoDock Vina by changing the empirical parameters
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HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra‐large chemical libraries
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zPoseScore model for accurate and robust protein–ligand docking pose scoring in CASP15
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Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies
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Key Aspects for Achieving Hits by Virtual Screening Studies
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Deep Learning Strategies for Enhanced Molecular Docking and Virtual Screening
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Integrated In Silico Pipeline for Validating AI-Generated Ligands: From Docking Consensus to Molecular Dynamics
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Exploring biogenic chalcones as DprE1 inhibitors for antitubercular activity via in silico approach
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Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges
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Druggable hot spots in trypanothione reductase: novel insights and opportunities for drug discovery revealed by DRUGpy
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Discovery of inhibitors targeting protein tyrosine phosphatase 1B using a combined virtual screening approach
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Fundamentals of molecular docking: Concepts, terminology, docking types and scoring functions
inferred← citeshttps://doi.org/10.1016/j.bpj.2023.11.1203openalex-snapshot
PopShift: A thermodynamically sound approach to estimate binding free energies by accounting for ligand-induced population shifts from a ligand-free MSM
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Edaravone N-benzyl pyridinium derivatives: BACE-1 inhibition, kinetics and in silico binding pose determination
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Rational protein engineering of α-L-arabinofuranosidase from Aspergillus niger for improved catalytic hydrolysis efficiency on kenaf hemicellulose
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Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01436openalex-snapshot
Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain Flexibility
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00549openalex-snapshot
Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features
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Conformer Generation for Structure-Based Drug Design: How Many and How Good?
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00669openalex-snapshot
Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00905openalex-snapshot
Benchmarking Cross-Docking Strategies in Kinase Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01715openalex-snapshot
DSDPFlex: Flexible-Receptor Docking with GPU Acceleration
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Benchmarking Docking Protocols on Predicting Alternative Binding Modes
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Native Contact Ratio as a Topological Metric for Machine Learning Based Molecular Docking
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PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model
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Ligand-Conditioned Side Chain Packing for Flexible Molecular Docking
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A human metabolic map of pharmacological perturbations reveals drug modes of action
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A 3D structural SARS-CoV-2–human interactome to explore genetic and drug perturbations
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Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors
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Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 MPRO
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Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling
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Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches
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Alchemical approach performance in calculating the ligand-binding free energy
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Flexible protein–ligand docking with diffusion-based side-chain packing
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Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term
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A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
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A new paradigm for applying deep learning to protein–ligand interaction prediction
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Discussions on the generalization of HybridSP on more equivalent benchmarks
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Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery
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Fully flexible molecular alignment enables accurate ligand structure modelling
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PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking
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ApoDock: Ligand-Conditioned Sidechain Packing for Flexible Molecular Docking
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GNINA 1.0: molecular docking with deep learning
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Improving Reliability of the Virtual Screening Process for Human Dihydroorotate Dehydrogenase Enzyme by Combination of the Ensemble and Consensus Docking Approaches
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PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking
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PackDock is a Flexible Protein-Ligand Docking Approach with Diffusion-Based Side Chain Packing
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zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15
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Improving the Accuracy of AutoDock Vina by Changing the Empirical Parameters
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DSDPFlex: An Improved Flexible-Receptor Docking Method with GPU Acceleration
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DSDPFlex: An Improved Flexible-Receptor Docking Method with GPU Acceleration
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Deep Learning Strategies for Enhanced Molecular Docking and Virtual Screening
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Deep Learning Strategies for Enhanced Molecular Docking and Virtual Screening
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Condensing Molecular Docking CNNs via Knowledge Distillation
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GNINA 1.0: Molecular Docking with Deep Learning
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Large scale prospective evaluation of co-folding across hundreds of Mac1-ligand complexes and three virtual screens
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