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Comparative Assessment of Scoring Functions on a Diverse Test Set

https://doi.org/10.1021/ci9000053
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What this work says

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

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A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
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General and targeted statistical potentials for protein–ligand interactions
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A statistical rescoring scheme for protein–ligand docking: Consideration of entropic effect
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Automated docking of substrates to proteins by simulated annealing
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A Fast Flexible Docking Method using an Incremental Construction Algorithm
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Knowledge-based scoring function to predict protein-ligand interactions
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Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4
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The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure
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Recent developments in structure-based drug design
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SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex
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Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search
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Calculations of protein-ligand binding entropy of relative and overall molecular motions
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Structure-based drug design
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Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming
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Target-biased scoring approaches and expert systems in structure-based virtual screening
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Virtual screening strategies in drug discovery
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A practical approach to docking of zinc metalloproteinase inhibitors
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LigScore: a novel scoring function for predicting binding affinities
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Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation
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Structure-based drug design
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Recent advances in structure-based rational drug design
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Structure-based drug design: progress, results and challenges
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Consensus scoring for ligand/protein interactions
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LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites
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Structure-based virtual screening: an overview
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Reduced Dimensionality in Ligand—Protein Structure Prediction: Covalent Inhibitors of Serine Proteases and Design of Site-Directed Combinatorial Libraries
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How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment
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ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
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An Extensive Test of 14 Scoring Functions Using the PDBbind Refined Set of 800 Protein−Ligand Complexes
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Consensus Scoring Criteria for Improving Enrichment in Virtual Screening
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On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors
inferredcites →https://doi.org/10.1021/ci700028wopenalex-snapshot
Automatic Perception of Organic Molecules Based on Essential Structural Information
inferredcites →https://doi.org/10.1021/ci7000346openalex-snapshot
Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets
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Free energy calculations: Applications to chemical and biochemical phenomena
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An Extended Linear Response Method for Determining Free Energies of Hydration
inferredcites →https://doi.org/10.1021/jm0003992openalex-snapshot
Detailed Analysis of Scoring Functions for Virtual Screening
inferredcites →https://doi.org/10.1021/jm001044lopenalex-snapshot
Protein-Based Virtual Screening of Chemical Databases. 1. Evaluation of Different Docking/Scoring Combinations
inferredcites →https://doi.org/10.1021/jm0203783openalex-snapshot
Comparative Evaluation of 11 Scoring Functions for Molecular Docking
inferredcites →https://doi.org/10.1021/jm020406hopenalex-snapshot
Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine
inferredcites →https://doi.org/10.1021/jm0302997openalex-snapshot
Evaluation of Docking Performance: Comparative Data on Docking Algorithms
inferredcites →https://doi.org/10.1021/jm030489hopenalex-snapshot
Assessing Scoring Functions for Protein−Ligand Interactions
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/jm0306430openalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy
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The PDBbind Database: Methodologies and Updates
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Large-Scale Validation of a Quantum Mechanics Based Scoring Function: Predicting the Binding Affinity and the Binding Mode of a Diverse Set of Protein−Ligand Complexes
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Structure-based Drug Discovery Using GPCR Homology Modeling: Successful Virtual Screening for Antagonists of the Alpha1A Adrenergic Receptor
inferredcites →https://doi.org/10.1021/jm049314dopenalex-snapshot
A Knowledge-Based Energy Function for Protein−Ligand, Protein−Protein, and Protein−DNA Complexes
inferredcites →https://doi.org/10.1021/jm049798dopenalex-snapshot
Comparison of Automated Docking Programs as Virtual Screening Tools
inferredcites →https://doi.org/10.1021/jm050038sopenalex-snapshot
PMF Scoring Revisited
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A Critical Assessment of Docking Programs and Scoring Functions
inferredcites →https://doi.org/10.1021/jm050436vopenalex-snapshot
DrugScore CSD Knowledge-Based Scoring Function Derived from Small Molecule Crystal Data with Superior Recognition Rate of Near-Native Ligand Poses and Better Affinity Prediction
inferredcites →https://doi.org/10.1021/jm051256oopenalex-snapshot
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes
inferredcites →https://doi.org/10.1021/jm0608356openalex-snapshot
Benchmarking Sets for Molecular Docking
inferredcites →https://doi.org/10.1021/jm061277yopenalex-snapshot
Diverse, High-Quality Test Set for the Validation of Protein−Ligand Docking Performance
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A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
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Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins
inferredcites →https://doi.org/10.1023/a:1007996124545openalex-snapshot
Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
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Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs
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A knowledge-based scoring function for protein-ligand interactions: Probing the reference state
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Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding
inferredcites →https://doi.org/10.1023/a:1011115820450openalex-snapshot
DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
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Further development and validation of empirical scoring functions for structure-based binding affinity prediction
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Comparative study of several algorithms for flexible ligand docking
inferredcites →https://doi.org/10.1023/b:jcam.0000047812.39758.abopenalex-snapshot
Evaluation and application of multiple scoring functions for a virtual screening experiment
inferredcites →https://doi.org/10.1038/384023a0openalex-snapshot
Signalling the fat controller
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Virtual screening of chemical libraries
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Structures-based drug design ten years on
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Predicting protein–ligand binding affinities: a low scoring game?
inferredcites →https://doi.org/10.1093/nar/28.1.235openalex-snapshot
The Protein Data Bank
inferredcites →https://doi.org/10.1093/protein/7.3.385openalex-snapshot
A new method for predicting binding affinity in computer-aided drug design
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The Many Roles of Computation in Drug Discovery
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Calculation of Protein-Ligand Binding Affinities
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STRUCTURE-BASED DRUG DESIGN:Computational Advances

What points at this work

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

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A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function
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Exploring the free-energy landscape of carbohydrate–protein complexes: development and validation of scoring functions considering the binding-site topology
inferred← citeshttps://doi.org/10.1007/s10822-014-9827-yopenalex-snapshot
A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach
inferred← citeshttps://doi.org/10.1007/s10822-016-0005-2openalex-snapshot
Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations
inferred← citeshttps://doi.org/10.1007/s10822-016-9943-yopenalex-snapshot
Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015
inferred← citeshttps://doi.org/10.1007/s10822-016-9960-xopenalex-snapshot
A D3R prospective evaluation of machine learning for protein-ligand scoring
inferred← citeshttps://doi.org/10.1007/s10822-017-0030-9openalex-snapshot
GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking
inferred← citeshttps://doi.org/10.1007/s10822-017-0038-1openalex-snapshot
Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015
inferred← citeshttps://doi.org/10.1007/s10822-017-0068-8openalex-snapshot
Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization
inferred← citeshttps://doi.org/10.1007/s10822-017-0069-7openalex-snapshot
Protein–ligand docking using FFT based sampling: D3R case study
inferred← citeshttps://doi.org/10.1007/s10822-017-0082-xopenalex-snapshot
Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor
inferred← citeshttps://doi.org/10.1007/s10822-018-0133-yopenalex-snapshot
Convolutional neural network scoring and minimization in the D3R 2017 community challenge
inferred← citeshttps://doi.org/10.1007/s10822-018-0160-8openalex-snapshot
In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery
inferred← citeshttps://doi.org/10.1007/s10822-018-0174-2openalex-snapshot
Could the presence of sodium ion influence the accuracy and precision of the ligand-posing in the human A2A adenosine receptor orthosteric binding site using a molecular docking approach? Insights from Dockbench
inferred← citeshttps://doi.org/10.1007/s10822-019-00185-0openalex-snapshot
Predicting protein–ligand binding modes for CELPP and GC3: workflows and insight
inferred← citeshttps://doi.org/10.1007/s10822-019-00201-3openalex-snapshot
Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures
inferred← citeshttps://doi.org/10.1007/s10822-019-00227-7openalex-snapshot
Assessing and improving the performance of consensus docking strategies using the DockBox package
inferred← citeshttps://doi.org/10.1007/s10822-019-00247-3openalex-snapshot
Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S
inferred← citeshttps://doi.org/10.1007/s10822-020-00305-1openalex-snapshot
Improving the binding affinity estimations of protein–ligand complexes using machine-learning facilitated force field method
inferred← citeshttps://doi.org/10.1007/s10822-020-00339-5openalex-snapshot
Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset
inferred← citeshttps://doi.org/10.1007/s10822-020-00340-yopenalex-snapshot
Prediction of ligand binding mode among multiple cross-docking poses by molecular dynamics simulations
inferred← citeshttps://doi.org/10.1007/s10822-021-00423-4openalex-snapshot
Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction
inferred← citeshttps://doi.org/10.1007/s10822-022-00456-3openalex-snapshot
Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA
inferred← citeshttps://doi.org/10.1007/s10822-026-00929-9openalex-snapshot
Evaluating molecular docking for binding affinity predictions: a systematic analysis of key parameters and the utility of AlphaFold2 structures for the Schrödinger dataset
inferred← citeshttps://doi.org/10.1007/s10895-015-1746-3openalex-snapshot
Spectroscopy and Molecular Modeling Study on the Interaction Between Mycophenolate Mofetil and Pepsin
inferred← citeshttps://doi.org/10.1007/s11030-010-9269-yopenalex-snapshot
Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study
inferred← citeshttps://doi.org/10.1007/s11030-021-10266-8openalex-snapshot
AI in drug development: a multidisciplinary perspective
inferred← citeshttps://doi.org/10.1007/s11095-013-1012-9openalex-snapshot
Emerging Topics in Structure-Based Virtual Screening
inferred← citeshttps://doi.org/10.1007/s12272-015-0640-5openalex-snapshot
Role of computer-aided drug design in modern drug discovery
inferred← citeshttps://doi.org/10.1007/s12539-016-0174-7openalex-snapshot
Identification of Novel Potential gp120 of HIV-1 Antagonist Using Per-Residue Energy Contribution-Based Pharmacophore modelling
inferred← citeshttps://doi.org/10.1007/s12551-013-0130-2openalex-snapshot
Receptor–ligand molecular docking
inferred← citeshttps://doi.org/10.1007/s12672-026-04657-2openalex-snapshot
Elucidating EGCG’ s targets in nasopharyngeal carcinoma through combined cell-based assays and transcriptomic sequencing
inferred← citeshttps://doi.org/10.1007/s13596-019-00418-5openalex-snapshot
In vitro and in silico studies of the antifungal properties of the bulb and leaves extracts of Drimia delagoensis Baker (Jessop)
inferred← citeshttps://doi.org/10.1016/b978-0-12-409547-2.12352-2openalex-snapshot
Molecular Docking—A Solved Problem?
inferred← citeshttps://doi.org/10.1016/b978-0-12-809450-1.00002-8openalex-snapshot
Computer-Aided Discovery of Glycogen Phosphorylase Inhibitors Exploiting Natural Products
inferred← citeshttps://doi.org/10.1016/b978-0-12-818480-6.00008-4openalex-snapshot
New approaches to antibacterial drug discovery
inferred← citeshttps://doi.org/10.1016/b978-0-12-819497-3.00015-5openalex-snapshot
In silico development of quorum sensing inhibitors
inferred← citeshttps://doi.org/10.1016/b978-0-12-821748-1.00001-4openalex-snapshot
Impact of chemoinformatics approaches and tools on current chemical research
inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00001-1openalex-snapshot
Best Practices for Docking-Based Virtual Screening
inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00004-7openalex-snapshot
Post-processing of Docking Results: Tools and Strategies
inferred← citeshttps://doi.org/10.1016/b978-0-443-29808-0.00016-9openalex-snapshot
Molecular docking—A solved problem?
inferred← citeshttps://doi.org/10.1016/b978-0-443-49253-2.00016-7openalex-snapshot
Molecular docking tools and their benchmarking: A practical guide for interdisciplinary researchers
inferred← citeshttps://doi.org/10.1016/bs.apha.2025.02.001openalex-snapshot
Harnessing machine learning for rational drug design
inferred← citeshttps://doi.org/10.1016/bs.arcc.2024.10.001openalex-snapshot
Machine learning and deep learning based scoring functions in deciphering ligand-receptor binding: An application in drug design for GPCRs
inferred← citeshttps://doi.org/10.1016/bs.armc.2022.08.008openalex-snapshot
Can docking scoring functions guarantee success in virtual screening?
inferred← citeshttps://doi.org/10.1016/bs.mcb.2015.11.002openalex-snapshot
Computational methods for studying G protein-coupled receptors (GPCRs)
inferred← citeshttps://doi.org/10.1016/bs.pmch.2021.01.004openalex-snapshot
Use of molecular docking computational tools in drug discovery
inferred← citeshttps://doi.org/10.1016/j.aichem.2023.100020openalex-snapshot
Machine learning small molecule properties in drug discovery
inferred← citeshttps://doi.org/10.1016/j.artmed.2015.02.002openalex-snapshot
Machine learning in computational docking
inferred← citeshttps://doi.org/10.1016/j.bioorg.2015.02.003openalex-snapshot
2,4-dihydroxy benzaldehyde derived Schiff bases as small molecule Hsp90 inhibitors: Rational identification of a new anticancer lead
inferred← citeshttps://doi.org/10.1016/j.bioorg.2025.108539openalex-snapshot
Integrating untargeted metabolomics and computational docking for biomarker evaluation: A case study on marine algae-derived ligands
inferred← citeshttps://doi.org/10.1016/j.biopha.2014.01.003openalex-snapshot
Molecular docking study, synthesis and biological evaluation of Schiff bases as Hsp90 inhibitors
inferred← citeshttps://doi.org/10.1016/j.bmc.2014.04.057openalex-snapshot
Identification and characterization of a new reversible MAGL inhibitor
inferred← citeshttps://doi.org/10.1016/j.bmcl.2010.05.049openalex-snapshot
Interaction of aminoadamantane derivatives with the influenza A virus M2 channel-Docking using a pore blocking model
inferred← citeshttps://doi.org/10.1016/j.bmcl.2013.10.044openalex-snapshot
Comprehending renin inhibitor’s binding affinity using structure-based approaches
inferred← citeshttps://doi.org/10.1016/j.bmcl.2016.07.024openalex-snapshot
Ligand binding cooperativity: Bioisosteric replacement of C O with SO2 among thrombin inhibitors
inferred← citeshttps://doi.org/10.1016/j.bmcl.2017.01.001openalex-snapshot
Discovery of furan carboxylate derivatives as novel inhibitors of ATP-citrate lyase via virtual high-throughput screening
inferred← citeshttps://doi.org/10.1016/j.bpj.2010.02.034openalex-snapshot
Improved Ligand-Protein Binding Affinity Predictions Using Multiple Binding Modes
inferred← citeshttps://doi.org/10.1016/j.carres.2013.09.004openalex-snapshot
Azido derivatives of cellobiose: oxidation at C1 with cellobiose dehydrogenase from Sclerotium rolfsii
inferred← citeshttps://doi.org/10.1016/j.cbpa.2011.06.005openalex-snapshot
Large-scale integrated super-computing platform for next generation virtual drug discovery
inferred← citeshttps://doi.org/10.1016/j.commatsci.2025.113904openalex-snapshot
Hyperparameter optimization and neural architecture search algorithms for graph Neural Networks in cheminformatics
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2016.04.005openalex-snapshot
Machine learning optimization of cross docking accuracy
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107250openalex-snapshot
Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.cplett.2025.142372openalex-snapshot
Prediction of the hydration structures at the protein–protein Interface of dimers using deep learning
inferred← citeshttps://doi.org/10.1016/j.csbj.2021.03.004openalex-snapshot
A review on compound-protein interaction prediction methods: Data, format, representation and model
inferred← citeshttps://doi.org/10.1016/j.ddtec.2010.12.003openalex-snapshot
Docking compared to 3D-pharmacophores: the scoring function challenge
inferred← citeshttps://doi.org/10.1016/j.ddtec.2020.09.001openalex-snapshot
Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.12.025openalex-snapshot
Post-docking virtual screening of diverse binding pockets: Comparative study using DOCK, AMMOS, X-Score and FRED scoring functions
inferred← citeshttps://doi.org/10.1016/j.ejmech.2013.07.022openalex-snapshot
Toward the design of new DNA G-quadruplex ligands through rational analysis of polymorphism and binding data
inferred← citeshttps://doi.org/10.1016/j.ejmech.2015.03.059openalex-snapshot
Binding cooperativity between a ligand carbonyl group and a hydrophobic side chain can be enhanced by additional H-bonds in a distance dependent manner: A case study with thrombin inhibitors
inferred← citeshttps://doi.org/10.1016/j.ejmech.2021.113705openalex-snapshot
Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review
inferred← citeshttps://doi.org/10.1016/j.ejmech.2026.118576openalex-snapshot
Structure-based virtual screening of ultra-large chemical spaces: Advances and pitfalls
inferred← citeshttps://doi.org/10.1016/j.engappai.2015.06.021openalex-snapshot
Comparative assessment of machine-learning scoring functions on PDBbind 2013
inferred← citeshttps://doi.org/10.1016/j.exger.2014.05.007openalex-snapshot
Beta-caryophyllene modulates expression of stress response genes and mediates longevity in Caenorhabditis elegans
inferred← citeshttps://doi.org/10.1016/j.foodchem.2023.136912openalex-snapshot
Active peptides with hypoglycemic effect obtained from hemp (Cannabis sativa L) protein through identification, molecular docking, and virtual screening
inferred← citeshttps://doi.org/10.1016/j.foodchem.2023.138129openalex-snapshot
Identification and molecular binding mechanism of novel pancreatic lipase and cholesterol esterase inhibitory peptides from heat-treated adzuki bean protein hydrolysates
inferred← citeshttps://doi.org/10.1016/j.ijbiomac.2015.06.039openalex-snapshot
Molecular docking study, synthesis and biological evaluation of Mannich bases as Hsp90 inhibitors
inferred← citeshttps://doi.org/10.1016/j.insi.2026.100451openalex-snapshot
ML-guided prioritization in virtual screening: Docking-supervised, potency-supervised, and hybrid strategies
inferred← citeshttps://doi.org/10.1016/j.jbiotec.2019.01.023openalex-snapshot
Structure based virtual screening of novel noncompetitive antagonist of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2012.12.011openalex-snapshot
Toward an understanding of the sequence and structural basis of allosteric proteins
inferred← citeshttps://doi.org/10.1016/j.jmgm.2014.07.010openalex-snapshot
An electronic environment and contact direction sensitive scoring function for predicting affinities of protein–ligand complexes in Contour®
inferred← citeshttps://doi.org/10.1016/j.jmgm.2015.01.009openalex-snapshot
Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets
inferred← citeshttps://doi.org/10.1016/j.jmgm.2015.05.004openalex-snapshot
Studying the binding interactions of allosteric agonists and antagonists of the CXCR4 receptor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2018.06.005openalex-snapshot
Visualizing convolutional neural network protein-ligand scoring
inferred← citeshttps://doi.org/10.1016/j.micpath.2018.05.016openalex-snapshot
Antibacterial activity of flavonoid isolated from Trianthema decandra against Pseudomonas aeruginosa and molecular docking study of FabZ
inferred← citeshttps://doi.org/10.1016/j.molliq.2022.119230openalex-snapshot
Cu(II) complexes of flavonoids in solution: Impact of the Cu(II) ion on the antioxidant and DNA-intercalating properties
inferred← citeshttps://doi.org/10.1016/j.molstruc.2023.134982openalex-snapshot
A convergent multicomponent synthesis, spectral analysis, molecular modelling and docking studies of novel 2H-pyrido[1,2-a]pyrimidine-2,4(3H)-dione derivatives as potential anti-cervical cancer agents
inferred← citeshttps://doi.org/10.1016/j.molstruc.2023.135409openalex-snapshot
Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease
inferred← citeshttps://doi.org/10.1016/j.sbi.2026.103257openalex-snapshot
More protein-ligand data are needed for AlphaFold-like models to enable drug discovery
inferred← citeshttps://doi.org/10.1016/j.tox.2022.153412openalex-snapshot
Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking
inferred← citeshttps://doi.org/10.1016/j.ymeth.2014.11.015openalex-snapshot
Benchmarking methods and data sets for ligand enrichment assessment in virtual screening
inferred← citeshttps://doi.org/10.1021/acs.accounts.4c00093openalex-snapshot
Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening
inferred← citeshttps://doi.org/10.1021/acs.accounts.6b00491openalex-snapshot
Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions
inferred← citeshttps://doi.org/10.1021/acs.chemrev.3c00042openalex-snapshot
New Insights into the Cooperativity and Dynamics of Dimeric Enzymes
inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jafc.1c06110openalex-snapshot
Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00026openalex-snapshot
Binding Affinity Prediction by Pairwise Function Based on Neural Network
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00074openalex-snapshot
Discovery of Targeted Covalent Natural Products against PLK1 by Herb-Based Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01010openalex-snapshot
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00096openalex-snapshot
Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00292openalex-snapshot
True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c01443openalex-snapshot
Discovery of Novel Acetylcholinesterase Inhibitors by Virtual Screening, In Vitro Screening, and Molecular Dynamics Simulations
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00278openalex-snapshot
Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein–Ligand Sampling
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00485openalex-snapshot
Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein–Ligand Scoring Functions
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00697openalex-snapshot
EISA-Score: Element Interactive Surface Area Score for Protein–Ligand Binding Affinity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00705openalex-snapshot
MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00757openalex-snapshot
ULYSSES : An Efficient and Easy to Use Semiempirical Library for C++
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00987openalex-snapshot
gr Predictor: A Deep Learning Model for Predicting the Hydration Structures around Proteins
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01057openalex-snapshot
DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01149openalex-snapshot
Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00567openalex-snapshot
Water Network-Augmented Two-State Model for Protein–Ligand Binding Affinity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00776openalex-snapshot
DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein–Ligand Binding Affinity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00311openalex-snapshot
Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00825openalex-snapshot
CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01014openalex-snapshot
Normalized Protein–Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01116openalex-snapshot
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01175openalex-snapshot
CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein–Ligand Binding Interactions
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01902openalex-snapshot
GRADE and X-GRADE: Unveiling Novel Protein–Ligand Interaction Fingerprints Based on GRAIL Scores
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00158openalex-snapshot
Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00235openalex-snapshot
On-the-Fly Integration of Data from a Spin-Diffusion-Based NMR Experiment into Protein–Ligand Docking
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00279openalex-snapshot
Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00309openalex-snapshot
Evaluation of GalaxyDock Based on the Community Structure–Activity Resource 2013 and 2014 Benchmark Studies
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00338openalex-snapshot
Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00339openalex-snapshot
Predicting Binding Poses and Affinities in the CSAR 2013–2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential
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