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PHOENIX: A Scoring Function for Affinity Prediction Derived Using High-Resolution Crystal Structures and Calorimetry Measurements
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Use of Scoring Functions in Drug Discovery Applications
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Approaches to the Description and Prediction of the Binding Affinity of Small-Molecule Ligands to Macromolecular Receptors
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New and fast statistical‐thermodynamic method for computation of protein‐ligand binding entropy substantially improves docking accuracy
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Role of binding entropy in the refinement of protein–ligand docking predictions: Analysis based on the use of 11 scoring functions
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Principles of docking: An overview of search algorithms and a guide to scoring functions
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Critical evaluation of methods to incorporate entropy loss upon binding in high‐throughput docking
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A statistical rescoring scheme for protein–ligand docking: Consideration of entropic effect
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SFCscore: Scoring functions for affinity prediction of protein–ligand complexes
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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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Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming
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Improved Ligand-Protein Binding Affinity Predictions Using Multiple Binding Modes
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Long-Timescale Molecular-Dynamics Simulations of the Major Urinary Protein Provide Atomistic Interpretations of the Unusual Thermodynamics of Ligand Binding
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The Thermodynamics of Protein–Ligand Interaction and Solvation: Insights for Ligand Design
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Further development and validation of empirical scoring functions for structure-based binding affinity prediction
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inferredcites →https://doi.org/10.1111/j.1747-0285.2007.00519.xopenalex-snapshot
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