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Binding Affinity Prediction for Protein–Ligand Complexes Based on β Contacts and B Factor
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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(19981115)19:14<1639::aid-jcc10>3.0.co;2-bopenalex-snapshot
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
inferredcites →https://doi.org/10.1002/(sici)1097-0134(19981115)33:3<367::aid-prot6>3.0.co;2-wopenalex-snapshot
Flexible docking using tabu search and an empirical estimate of binding affinity
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/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.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/jcc.540130714openalex-snapshot
Validation of the general purpose QUANTA ®3.2/CHARMm® force field
inferredcites →https://doi.org/10.1002/pro.5560061208openalex-snapshot
Analysis of temperature factor distribution in high‐resolution protein structures
inferredcites →https://doi.org/10.1002/prot.20375openalex-snapshot
Prediction of protein B‐factor profiles
inferredcites →https://doi.org/10.1002/prot.20588openalex-snapshot
General and targeted statistical potentials for protein–ligand interactions
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/bf00124474openalex-snapshot
Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities
inferredcites →https://doi.org/10.1007/bf00124499openalex-snapshot
Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4
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/s008940050096openalex-snapshot
SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex
inferredcites →https://doi.org/10.1016/b978-0-444-87806-9.50013-xopenalex-snapshot
A Framework for Computational Morphology
inferredcites →https://doi.org/10.1016/j.jmgm.2004.11.007openalex-snapshot
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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Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets
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Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors
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Comparative Assessment of Scoring Functions on a Diverse Test Set
inferredcites →https://doi.org/10.1021/jm030489hopenalex-snapshot
Assessing Scoring Functions for Protein−Ligand Interactions
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/jm100112jopenalex-snapshot
A Medicinal Chemist’s Guide to Molecular Interactions
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/10.1023/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:1007999920146openalex-snapshot
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
inferredcites →https://doi.org/10.1023/a:1011115820450openalex-snapshot
DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
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/10.1039/c0ce00819bopenalex-snapshot
Recent advances in anion–π interactions
inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot
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inferredcites →https://doi.org/10.1093/proeng/gzg014openalex-snapshot
Flexibility analysis of enzyme active sites by crystallographic temperature factors
inferredcites →https://doi.org/10.1145/235815.235821openalex-snapshot
The quickhull algorithm for convex hulls
inferredcites →https://doi.org/10.1186/1471-2105-11-193openalex-snapshot
A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction
inferredcites →https://doi.org/10.1371/journal.pone.0059737openalex-snapshot
Beta Atomic Contacts: Identifying Critical Specific Contacts in Protein Binding Interfaces
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781394167258.ch1openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9781394167258.ch2openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jcc.24667openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jmr.2471openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/med.21764openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.202000144openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.70010openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1225openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1429openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1567openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-10-5187-6_1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00530-024-01650-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00530-026-02570-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00726-018-2574-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-020-00305-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-021-00423-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-021-00434-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11030-021-10266-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11030-021-10274-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11030-022-10489-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11030-026-11670-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12539-019-00327-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12539-024-00644-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12539-025-00745-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00009-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/bs.armc.2022.08.008openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.artmed.2015.02.002openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.biochi.2014.10.017openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2023.107972openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.csbj.2022.02.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.csbj.2024.03.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01841openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00848openalex-snapshot
RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00190openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00427openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jpcb.7b11737openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci500081mopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/jacs.0c09799openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/jacs.9b06275openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/srep14214openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/17460441.2021.1929921openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbaa107openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbab054openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbab476openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btab132openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btu281openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btu626openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/nar/gkv315openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/protein/gzu044openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2018/6565241openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2019/8427042openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1471-2105-15-291openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1471-2105-15-57openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1471-2105-15-s16-s3openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13321-023-00795-9openalex-snapshot
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inferred← citeshttps://doi.org/10.12677/biphy.2024.122003openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pcbi.1013074openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0144486openalex-snapshot
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inferred← citeshttps://doi.org/10.2174/0929867325666180508100436openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/ijms20184574openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/molecules200610947openalex-snapshot
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inferred← citeshttps://doi.org/10.5455/javar.2021.h481openalex-snapshot
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inferred← citeshttps://doi.org/10.6084/m9.figshare.26660296openalex-snapshot
Additional file 1 of Structure-based, deep-learning models for protein-ligand binding affinity prediction
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