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Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein–Ligand Binding Geometry and Free Energy
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Critical evaluation of search algorithms for automated molecular docking and database screening
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Assessing energy functions for flexible docking
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Assessing search strategies for flexible docking
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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
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BLEEP?potential of mean force describing protein-ligand interactions: I. Generating potential
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BLEEP?potential of mean force describing protein-ligand interactions: II. Calculation of binding energies and comparison with experimental data
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Docking enzyme‐inhibitor complexes using a preference‐based free‐energy surface
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Study of a highly accurate and fast protein–ligand docking method based on molecular dynamics
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New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations
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Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm
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Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures
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CHARMM : A program for macromolecular energy, minimization, and dynamics calculations
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Molecular docking using shape descriptors
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Validation of the general purpose QUANTA ®3.2/CHARMm® force field
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A preference-based free-energy parameterization of enzyme-inhibitor binding. Applications to HIV-1-protease inhibitor design
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SFCscore: Scoring functions for affinity prediction of protein–ligand complexes
inferredcites →https://doi.org/10.1002/prot.340080302openalex-snapshot
Automated docking of substrates to proteins by simulated annealing
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
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Knowledge-based scoring function to predict protein-ligand interactions
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Encyclopedia of Computational Chemistry
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Bias, reporting, and sharing: computational evaluations of docking methods
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The HSP90 binding mode of a radicicol-like E-oxime determined by docking, binding free energy estimations, and NMR 15N chemical shifts
inferredcites →https://doi.org/10.1016/j.jmgm.2003.12.005openalex-snapshot
MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology
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Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
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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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An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics
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SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence
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Ligand−Protein DataBase: Linking Protein−Ligand Complex Structures to Binding Data
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Assessing Scoring Functions for Protein−Ligand Interactions
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Efficient Evaluation of Binding Free Energy Using Continuum Electrostatics Solvation
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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
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Discovery of Kinase Inhibitors by High-Throughput Docking and Scoring Based on a Transferable Linear Interaction Energy Model
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Combining Docking, Molecular Dynamics and the Linear Interaction Energy Method to Predict Binding Modes and Affinities for Non-nucleoside Inhibitors to HIV-1 Reverse Transcriptase
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Is Quantum Mechanics Necessary for Predicting Binding Free Energy?
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A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
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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:1016357811882openalex-snapshot
Further development and validation of empirical scoring functions for structure-based binding affinity prediction
inferredcites →https://doi.org/10.1073/pnas.96.18.9997openalex-snapshot
The maximal affinity of ligands
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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/10.1109/memb.2009.931789openalex-snapshot
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CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions
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ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions
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