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Classification of Current Scoring Functions
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1096-987x(19971115)18:14<1812::aid-jcc10>3.0.co;2-hopenalex-snapshot
Automated flexible ligand docking method and its application for database search
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Automated docking of flexible ligands: Applications of autodock
inferredcites →https://doi.org/10.1002/0471224413openalex-snapshot
Reviews in Computational Chemistry
inferredcites →https://doi.org/10.1002/0471224413.ch1openalex-snapshot
Small Molecule Docking and Scoring
inferredcites →https://doi.org/10.1002/0471433519.ch2openalex-snapshot
The Use of Scoring Functions in Drug Discovery Applications
inferredcites →https://doi.org/10.1002/1096-987x(200103)22:4<418::aid-jcc1012>3.0.co;2-3openalex-snapshot
Effect of ligand volume correction on PMF scoring
inferredcites →https://doi.org/10.1002/1521-3773(20020802)41:15<2644::aid-anie2644>3.0.co;2-oopenalex-snapshot
Approaches to the Description and Prediction of the Binding Affinity of Small-Molecule Ligands to Macromolecular Receptors
inferredcites →https://doi.org/10.1002/jcc.20047openalex-snapshot
Binding affinity prediction with different force fields: Examination of the linear interaction energy method
inferredcites →https://doi.org/10.1002/jcc.20504openalex-snapshot
An iterative knowledge‐based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials
inferredcites →https://doi.org/10.1002/jcc.20505openalex-snapshot
An iterative knowledge‐based scoring function to predict protein–ligand interactions: II. Validation of the scoring function
inferredcites →https://doi.org/10.1002/jcc.540040211openalex-snapshot
CHARMM : A program for macromolecular energy, minimization, and dynamics calculations
inferredcites →https://doi.org/10.1002/jcc.540070216openalex-snapshot
An all atom force field for simulations of proteins and nucleic acids
inferredcites →https://doi.org/10.1002/jcc.540130412openalex-snapshot
Automated docking with grid‐based energy evaluation
inferredcites →https://doi.org/10.1002/prot.10465openalex-snapshot
Improved protein–ligand docking using GOLD
inferredcites →https://doi.org/10.1002/prot.20512openalex-snapshot
Binding MOAD (Mother Of All Databases)
inferredcites →https://doi.org/10.1006/jmbi.1996.0175openalex-snapshot
Statistical Potentials Extracted From Protein Structures: How Accurate Are They?
inferredcites →https://doi.org/10.1006/jmbi.1996.0897openalex-snapshot
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
inferredcites →https://doi.org/10.1006/jmbi.1999.3371openalex-snapshot
Knowledge-based scoring function to predict protein-ligand interactions
inferredcites →https://doi.org/10.1007/0-306-46883-2_7openalex-snapshot
A knowledge-based scoring function for protein-ligand interactions: Probing the reference state
inferredcites →https://doi.org/10.1007/bf00126743openalex-snapshot
The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure
inferredcites →https://doi.org/10.1007/s00894-013-2005-7openalex-snapshot
A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions
inferredcites →https://doi.org/10.1016/0959-440x(95)80081-6openalex-snapshot
Knowledge-based potentials for proteins
inferredcites →https://doi.org/10.1016/j.ddtec.2004.08.004openalex-snapshot
Scoring functions for protein–ligand interactions: a critical perspective
inferredcites →https://doi.org/10.1016/s1074-5521(97)90251-9openalex-snapshot
A new class of models for computing receptor-ligand binding affinities
inferredcites →https://doi.org/10.1021/ar000033jopenalex-snapshot
Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models
inferredcites →https://doi.org/10.1021/ar970146bopenalex-snapshot
From Knowledge-Based Potentials to Combinatorial Lead Design in Silico
inferredcites →https://doi.org/10.1021/ci034246+openalex-snapshot
Predicting Protein−Ligand Binding Affinities Using Novel Geometrical Descriptors and Machine-Learning Methods
inferredcites →https://doi.org/10.1021/ci050422yopenalex-snapshot
SLICK − Scoring and Energy Functions for Protein−Carbohydrate Interactions
inferredcites →https://doi.org/10.1021/ci100244vopenalex-snapshot
NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
inferredcites →https://doi.org/10.1021/ci200269qopenalex-snapshot
CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions
inferredcites →https://doi.org/10.1021/ci200274qopenalex-snapshot
DSX: A Knowledge-Based Scoring Function for the Assessment of Protein–Ligand Complexes
inferredcites →https://doi.org/10.1021/ci2003889openalex-snapshot
NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
inferredcites →https://doi.org/10.1021/ci2003939openalex-snapshot
Knowledge-Based Scoring Functions in Drug Design: 3. A Two-Dimensional Knowledge-Based Hydrogen-Bonding Potential for the Prediction of Protein–Ligand Interactions
inferredcites →https://doi.org/10.1021/ci300493wopenalex-snapshot
ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions
inferredcites →https://doi.org/10.1021/ci300619xopenalex-snapshot
Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein–Ligand Interactions
inferredcites →https://doi.org/10.1021/ci400025fopenalex-snapshot
CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series
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/ci400120bopenalex-snapshot
SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes
inferredcites →https://doi.org/10.1021/ci500080qopenalex-snapshot
Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set
inferredcites →https://doi.org/10.1021/ci500081mopenalex-snapshot
Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results
inferredcites →https://doi.org/10.1021/ci500091ropenalex-snapshot
Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
inferredcites →https://doi.org/10.1021/ci500406kopenalex-snapshot
Beware of Machine Learning-Based Scoring Functions—On the Danger of Developing Black Boxes
inferredcites →https://doi.org/10.1021/ci5004152openalex-snapshot
Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function
inferredcites →https://doi.org/10.1021/ci600357topenalex-snapshot
Novel, Customizable Scoring Functions, Parameterized Using N-PLS, for Structure-Based Drug Discovery
inferredcites →https://doi.org/10.1021/ci8001167openalex-snapshot
MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening
inferredcites →https://doi.org/10.1021/ci9000053openalex-snapshot
Comparative Assessment of Scoring Functions on a Diverse Test Set
inferredcites →https://doi.org/10.1021/ci9002987openalex-snapshot
Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein−Ligand Interactions
inferredcites →https://doi.org/10.1021/cr800551wopenalex-snapshot
Theory of Free Energy and Entropy in Noncovalent Binding
inferredcites →https://doi.org/10.1021/ct4005992openalex-snapshot
The Movable Type Method Applied to Protein–Ligand Binding
inferredcites →https://doi.org/10.1021/j100026a034openalex-snapshot
An Extended Linear Response Method for Determining Free Energies of Hydration
inferredcites →https://doi.org/10.1021/ja00124a002openalex-snapshot
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
inferredcites →https://doi.org/10.1021/ja00315a051openalex-snapshot
A new force field for molecular mechanical simulation of nucleic acids and proteins
inferredcites →https://doi.org/10.1021/ja01062a035openalex-snapshot
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
inferredcites →https://doi.org/10.1021/ja960751uopenalex-snapshot
SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence
inferredcites →https://doi.org/10.1021/ja984102popenalex-snapshot
Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model
inferredcites →https://doi.org/10.1021/jm00014a020openalex-snapshot
Prediction of Drug Binding Affinities by Comparative Binding Energy Analysis
inferredcites →https://doi.org/10.1021/jm030331xopenalex-snapshot
Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding 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/jm030596bopenalex-snapshot
Simple, Intuitive Calculations of Free Energy of Binding for Protein−Ligand Complexes. 3. The Free Energy Contribution of Structural Water Molecules in HIV-1 Protease Complexes
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
inferredcites →https://doi.org/10.1021/jm048973nopenalex-snapshot
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
inferredcites →https://doi.org/10.1021/jm049081qopenalex-snapshot
Validation and Use of the MM-PBSA Approach for Drug Discovery
inferredcites →https://doi.org/10.1021/jm0497343openalex-snapshot
A Combined QM/MM Approach to Protein−Ligand Interactions: Polarization Effects of the HIV-1 Protease on Selected High Affinity Inhibitors
inferredcites →https://doi.org/10.1021/jm050260xopenalex-snapshot
Development of Quantitative Structure−Binding Affinity Relationship Models Based on Novel Geometrical Chemical Descriptors of the Protein−Ligand Interfaces
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/jm060999mopenalex-snapshot
Prediction of Protein−Ligand Interactions. Docking and Scoring: Successes and Gaps
inferredcites →https://doi.org/10.1021/jm300131xopenalex-snapshot
The Experimental Uncertainty of Heterogeneous Public Ki Data
inferredcites →https://doi.org/10.1021/jm800242qopenalex-snapshot
Is Quantum Mechanics Necessary for Predicting Binding Free Energy?
inferredcites →https://doi.org/10.1021/jm960684eopenalex-snapshot
Binding Affinities for Sulfonamide Inhibitors with Human Thrombin Using Monte Carlo Simulations with a Linear Response Method
inferredcites →https://doi.org/10.1021/jm980536jopenalex-snapshot
A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
inferredcites →https://doi.org/10.1021/ma00145a039openalex-snapshot
Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation
inferredcites →https://doi.org/10.1021/ma60054a013openalex-snapshot
Medium- and Long-Range Interaction Parameters between Amino Acids for Predicting Three-Dimensional Structures of 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
inferredcites →https://doi.org/10.1023/a:1008040323669openalex-snapshot
Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model
inferredcites →https://doi.org/10.1023/a:1008729005958openalex-snapshot
A knowledge-based scoring function for protein-ligand interactions: Probing the reference state
inferredcites →https://doi.org/10.1023/a:1016357811882openalex-snapshot
Further development and validation of empirical scoring functions for structure-based binding affinity prediction
inferredcites →https://doi.org/10.1038/nsb1203-980openalex-snapshot
Announcing the worldwide Protein Data Bank
inferredcites →https://doi.org/10.1038/nsb889openalex-snapshot
Active site electronic structure and dynamics during metalloenzyme catalysis
inferredcites →https://doi.org/10.1063/1.2811680openalex-snapshot
Introduction to Modern Statistical Mechanics
inferredcites →https://doi.org/10.1063/1.474725openalex-snapshot
Statistical potentials extracted from protein structures: Are these meaningful potentials?
inferredcites →https://doi.org/10.1073/pnas.0803847105openalex-snapshot
Application of a polarizable force field to calculations of relative protein–ligand binding affinities
inferredcites →https://doi.org/10.1073/pnas.102649299openalex-snapshot
Energetics of glycerol conduction through aquaglyceroporin GlpF
inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
inferredcites →https://doi.org/10.1093/bioinformatics/btu626openalex-snapshot
PDB-wide collection of binding data: current status of the PDBbind database
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/gkm911openalex-snapshot
Binding MOAD, a high-quality protein ligand database
inferredcites →https://doi.org/10.1093/nar/gkp456openalex-snapshot
PubChem: a public information system for analyzing bioactivities of small molecules
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1093/nar/gkt1031openalex-snapshot
The ChEMBL bioactivity database: an update
inferredcites →https://doi.org/10.1093/protein/7.3.385openalex-snapshot
A new method for predicting binding affinity in computer-aided drug design
inferredcites →https://doi.org/10.1093/protein/8.7.677openalex-snapshot
Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity
inferredcites →https://doi.org/10.1146/annurev.biophys.36.040306.132550openalex-snapshot
Calculation of Protein-Ligand Binding Affinities
inferredcites →https://doi.org/10.2174/1386207013330670openalex-snapshot
BindingDB: A Web-Accessible Molecular Recognition Database
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Structure‐Based Drug Design
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Docking, Scoring, and Virtual Screening in Drug Discovery
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Virtual Screening and Bioactivity Modeling for G Protein‐Coupled Receptors
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Structure‐Based Ultra‐Large Virtual Screenings
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DG‐GL: Differential geometry‐based geometric learning of molecular datasets
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GeauxDock: A novel approach for mixed‐resolution ligand docking using a descriptor‐based force field
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Flexible CDOCKER : Development and application of a pseudo‐explicit structure‐based docking method within CHARMM
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CIFDock : A novel CHARMM ‐based flexible receptor–flexible ligand docking protocol
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Do electrostatic interactions make a difference in physics‐based AutoDock4 scoring function?
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SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space
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Development of a machine learning‐based target‐specific scoring function for structure‐based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors
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Empirical Scoring Functions for Affinity Prediction of Protein‐ligand Complexes
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Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors
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ET‐score: Improving Protein‐ligand Binding Affinity Prediction Based on Distance‐weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm
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GB‐score: Minimally designed machine learning scoring function based on distance‐weighted interatomic contact features
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An ensemble‐based approach to estimate confidence of predicted protein–ligand binding affinity values
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Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect
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Physics‐Based Energy Functions for Computational Protein Design
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Applying molecular docking to pesticides
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Refining Molecular Docking Strategy for Aldose Reductase by Incorporating Specificity Pocket States and Methodological Evaluation
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Computational Intelligence Frameworks for High Throughput In Silico Mining of Antipathogenic Biomolecules for the Management of Plant Pathogens
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Recent advances in dynamic docking for drug discovery
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Ligand binding free energy and kinetics calculation in 2020
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Featurization strategies for protein–ligand interactions and their applications in scoring function development
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From Traditional Methods to Deep Learning Approaches: Advances in Protein–Protein Docking
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Virtual Screening in the Search of New and Potent Anti-Alzheimer Agents
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Feature Selection Investigation in Machine Learning Docking Scoring Functions
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Repurposing of Therapeutics for Anticancer Activity
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Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies
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A spatial-temporal graph attention network for protein–ligand binding affinity prediction based on molecular geometry
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A geometric rotation-equivariant spherical convolutional and gaussian radial basis network for predicting protein-ligand binding affinity
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Molecular docking in organic, inorganic, and hybrid systems: a tutorial review
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Biomolecular Topology: Modelling and Analysis
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An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
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Predicting Protein–Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph
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In silico investigation of 4-(Trifluoromethyl)benzohydrazide derivatives as potential anti-Alzheimer’s agents by targeting acetylcholinesterase and butyrylcholinesterase
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Recent Progress in Free Energy Methods
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In Silico Drug Design Methods for Drug Repurposing
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Molecular Docking: A Structure-Based Approach for Drug Repurposing
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Virtual screening of small-molecule libraries
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Virtual screening in drug discovery: a precious tool for a still-demanding challenge
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Virtual screening techniques in pharmaceutical research
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Post-processing of Docking Results: Tools and Strategies
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Advances in structure-based virtual screening for drug discovery
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Computational chemistry of natural product analogues
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Structure-based virtual screening in drug discovery
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Unraveling the neuroprotective potential of bioactive natural products: Insights from structure–activity studies inAlzheimer's disease
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Current advances on the development of BET inhibitors: insights from computational methods
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Can docking scoring functions guarantee success in virtual screening?
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Spectroscopic, electrochemical, and molecular docking studies of the interaction between the antihistamine drug desloratadine and dsDNA
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Experimental Uncertainty in Training Data for Protein-Ligand Binding Affinity Prediction Models
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Bio-inspired optimization for the molecular docking problem: State of the art, recent results and perspectives
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G-quadruplex virtual drug screening: A review
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De novo design with deep generative models based on 3D similarity scoring
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Rotamer Dynamics: Analysis of Rotamers in Molecular Dynamics Simulations of Proteins
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Further exploration of the quantitative distance-energy and contact number-energy relationships for predicting the binding affinity of protein-ligand complexes
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Ensemble of local and global information for Protein–Ligand Binding Affinity Prediction
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The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials
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Structure-based molecular modeling in SAR analysis and lead optimization
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Protein-ligand binding affinity prediction based on profiles of intermolecular contacts
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Harnessing systematic protein–ligand interaction fingerprints for drug discovery
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Advances in molecular glues: exploring chemical space and design principles for targeted protein degradation
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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
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Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review
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CADD, AI and ML in drug discovery: A comprehensive review
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An explainable machine learning-based scoring function using interpretable features and model explanation approaches for binding affinity prediction
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Structural and energetic evolution of fibrinogen toward to the betablocker interactions
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First evaluation of DNA binding interactions of the anti-Cancer drug Tofacitinib: An electrochemical and molecular docking approach
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Consensus scoring model for the molecular docking study of mTOR kinase inhibitor
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Application of computational methods for class A GPCR Ligand discovery
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Protein–ligand docking using differential evolution with an adaptive mechanism
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On importance of explicit account of non-complementary contacts in scoring functions
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8-benzimidazolyl coumarin-3-oxadiazoles – Synthesis, docking studies and Anti-proliferative evaluation against breast cancer
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COVID-19 and SARS-CoV-2. Modeling the present, looking at the future
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Convergence of computer-aided drug discovery and artificial intelligence: Towards next-generation therapeutics
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Role of structure-based drug design (SBDD) in the repurposing and discovery of anti-viral leads against Monkeypox virus disease
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A novel molecular docking program based on a multi-swarm competitive algorithm
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Uncovering and tackling fundamental limitations of compound potency predictions using machine learning models
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Docking is not enough: 17-trifluoromethylphenyl trinor PGF2α is only a very weak ligand of neurokinin-1 receptor
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Function and structure-based screening of compounds, peptides and proteins to identify drug candidates
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Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions
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Graph Neural Networks in Modern AI-Aided Drug Discovery
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Atomistic Simulations of Membrane Ion Channel Conduction, Gating, and Modulation
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A Self-Assembled Iron(II) Metallacage as a Trap for Per- and Polyfluoroalkyl Substances in Water
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.0c00474openalex-snapshot
Development of a New Scoring Function for Virtual Screening: APBScore
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00558openalex-snapshot
An ABSINTH-Based Protocol for Predicting Binding Affinities between Proteins and Small Molecules
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Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00737openalex-snapshot
Lin_F9: A Linear Empirical Scoring Function for Protein–Ligand Docking
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.2c00916openalex-snapshot
Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01251openalex-snapshot
Persistent Path-Spectral (PPS) Based Machine Learning for Protein–Ligand Binding Affinity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00567openalex-snapshot
Water Network-Augmented Two-State Model for Protein–Ligand Binding Affinity Prediction
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Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs
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Enhancing Generalizability in Protein–Ligand Binding Affinity Prediction with Multimodal Contrastive Learning
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HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery
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Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation
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Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function
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
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00427openalex-snapshot
Improved Prediction of Drug–Protein Interactions through Physics-Based Few-Shot Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01098openalex-snapshot
Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02505openalex-snapshot
ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00610openalex-snapshot
A Hybrid Knowledge-Based and Empirical Scoring Function for Protein–Ligand Interaction: SMoG2016
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00749openalex-snapshot
The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01550openalex-snapshot
Native Contact Ratio as a Topological Metric for Machine Learning Based Molecular Docking
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00049openalex-snapshot
Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein–Ligand Interactions
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00226openalex-snapshot
Rigidity Strengthening: A Mechanism for Protein–Ligand Binding
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00545openalex-snapshot
Comparative Assessment of Scoring Functions: The CASF-2016 Update
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00582openalex-snapshot
Converging a Knowledge-Based Scoring Function: DrugScore2018
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00712openalex-snapshot
In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00773openalex-snapshot
The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00334openalex-snapshot
AGL-Score: Algebraic Graph Learning Score for Protein–Ligand Binding Scoring, Ranking, Docking, and Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00370openalex-snapshot
Study of SHMT2 Inhibitors and Their Binding Mechanism by Computational Alanine Scanning
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00645openalex-snapshot
Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00714openalex-snapshot
Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
inferred← citeshttps://doi.org/10.1021/acs.jctc.9b01069openalex-snapshot
New Protocol for Predicting the Ligand-Binding Site and Mode Based on the 3D-RISM/KH Theory
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c00991openalex-snapshot
Boosting Protein–Ligand Binding Pose Prediction and Virtual Screening Based on Residue–Atom Distance Likelihood Potential and Graph Transformer
inferred← citeshttps://doi.org/10.1021/acsomega.2c05826openalex-snapshot
Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening
inferred← citeshttps://doi.org/10.1038/nprot.2017.114openalex-snapshot
Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark
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Metal–ligand interactions in drug design
inferred← citeshttps://doi.org/10.1038/s41598-023-34683-xopenalex-snapshot
Designing highly potent compounds using a chemical language model
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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
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Evaluating protein binding interfaces with transformer networks
inferred← citeshttps://doi.org/10.1038/s42256-023-00756-9openalex-snapshot
Learning characteristics of graph neural networks predicting protein–ligand affinities
inferred← citeshttps://doi.org/10.1038/s44386-025-00005-6openalex-snapshot
FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer
inferred← citeshttps://doi.org/10.1039/9781788010139-00281openalex-snapshot
Application of Molecular Modelling to Speed-up the Lead Discovery Process
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Computational Methods for the Discovery of Chemical Probes
inferred← citeshttps://doi.org/10.1039/c9cp06303jopenalex-snapshot
Combined strategies in structure-based virtual screening
inferred← citeshttps://doi.org/10.1039/d2cp04671gopenalex-snapshot
Hierarchical analysis of the target-based scoring function modification for the example of selected class A GPCRs
inferred← citeshttps://doi.org/10.1039/d2cp05644eopenalex-snapshot
An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties
inferred← citeshttps://doi.org/10.1039/d3ra01761copenalex-snapshot
Exploring the therapeutic mechanisms of Gleditsiae Spina acting on pancreatic cancer via network pharmacology, molecular docking and molecular dynamics simulation
inferred← citeshttps://doi.org/10.1039/d3sc02044dopenalex-snapshot
A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
inferred← citeshttps://doi.org/10.1039/d4dd00403eopenalex-snapshot
DeepRLI: a multi-objective framework for universal protein–ligand interaction prediction
inferred← citeshttps://doi.org/10.1039/d4md00869copenalex-snapshot
The physics-AI dialogue in drug design
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Modeling protein–ligand interactions for drug discovery in the era of deep learning
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Assessing the accuracy of binding pose prediction for kinase proteins and 7-azaindole inhibitors: a study with AutoDock4, Vina, DOCK 6, and GNINA 1.0
inferred← citeshttps://doi.org/10.1042/bst20211240openalex-snapshot
Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)
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Computational analysis for residue-specific CDK2-inhibitor bindings
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Molecular docking and molecular dynamics of hypoxia-inducible factor (HIF-1alpha): towards potential inhibitors
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An evaluation of the precision of computational methods used in drug development initiatives
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An integrated in silico and in vivo approach to determine the effects of three commonly used surfactants sodium dodecyl sulphate, cetylpyridinium chloride and sodium laureth sulphate on growth rate and hematology in Cyprinus carpio L
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Recent advances in automated protein design and its future challenges
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Binding affinity in drug design: experimental and computational techniques
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Computational representations of protein–ligand interfaces for structure-based virtual screening
inferred← citeshttps://doi.org/10.1080/17460441.2022.2114451openalex-snapshot
Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern
inferred← citeshttps://doi.org/10.1093/bib/bbaa410openalex-snapshot
Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
inferred← citeshttps://doi.org/10.1093/bib/bbaa411openalex-snapshot
Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design
inferred← citeshttps://doi.org/10.1093/bib/bbab054openalex-snapshot
Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction
inferred← citeshttps://doi.org/10.1093/bib/bbab127openalex-snapshot
Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction
inferred← citeshttps://doi.org/10.1093/bib/bbab136openalex-snapshot
Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction
inferred← citeshttps://doi.org/10.1093/bib/bbab527openalex-snapshot
Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design
inferred← citeshttps://doi.org/10.1093/bib/bbac603openalex-snapshot
Leveraging scaffold information to predict protein–ligand binding affinity with an empirical graph neural network
inferred← citeshttps://doi.org/10.1093/bib/bbae081openalex-snapshot
Prediction of protein–ligand binding affinity via deep learning models
inferred← citeshttps://doi.org/10.1093/bioadv/vbad155openalex-snapshot
Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa748openalex-snapshot
KORP-PL: a coarse-grained knowledge-based scoring function for protein–ligand interactions
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa982openalex-snapshot
Extended connectivity interaction features: improving binding affinity prediction through chemical description
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad502openalex-snapshot
Protein–ligand binding affinity prediction exploiting sequence constituent homology
inferred← citeshttps://doi.org/10.1093/bioinformatics/btw443openalex-snapshot
PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation
inferred← citeshttps://doi.org/10.1098/rsos.211745openalex-snapshot
A simple spatial extension to the extended connectivity interaction features for binding affinity prediction
inferred← citeshttps://doi.org/10.1101/2021.07.09.451643openalex-snapshot
Homology Modelling, In Silico Prediction And Characterization Of Cytochrome c oxidase In Cyprinus carpio And Tubifex tubifex And Molecular Docking Studies Between The Modelled Protein And Three Commonly Used Surfactants Sodium Dodecyl Sulphate, Cetylpyridinium Chloride And Sodium Laureth Sulphate
inferred← citeshttps://doi.org/10.1101/2022.02.09.479729openalex-snapshot
Identification of Catechins Binding Pockets in Monomeric A β 42 Through Ensemble Docking and MD Simulations
inferred← citeshttps://doi.org/10.1101/2023.01.03.522623openalex-snapshot
PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks
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A Structure-Based Computational Pipeline for Broad-Spectrum Antiviral Discovery
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DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction
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Five-Dimension Value-Based Quantitative Evaluation Model for Scientific and Technological Achievements Transformation Benefits of Power Grid Enterprises
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Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction
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Deep Learning-Driven Protein-Ligand Binding Affinity Prediction: Data, Architecture, Training and Evaluation
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Improving classical scoring functions using random forest: The non‐additivity of free energy terms’ contributions in binding
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The herbicide quinclorac as potent lipase inhibitor: Discovery via virtual screening and in vitro/in vivo validation
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