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A general approach for developing system‐specific functions to score protein–ligand docked complexes using support vector inductive logic programming
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Use of pair potentials across protein interfaces in screening predicted docked complexes
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Similarity-driven flexible ligand docking
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Structural analysis of inhibitor binding to human carbonic anhydrase II
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Principles of docking: An overview of search algorithms and a guide to scoring functions
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A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
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Assessment of CAPRI predictions in rounds 3–5 shows progress in docking procedures
inferredcites →https://doi.org/10.1002/qsar.200310005openalex-snapshot
Structure Activity Relationships (SAR) and Pharmacophore Discovery Using Inductive Logic Programming (ILP)
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Determination of atomic desolvation energies from the structures of crystallized proteins
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Modelling protein docking using shape complementarity, electrostatics and biochemical information 1 1Edited by J. Thornton
inferredcites →https://doi.org/10.1006/jmbi.1999.3371openalex-snapshot
Knowledge-based scoring function to predict protein-ligand interactions
inferredcites →https://doi.org/10.1007/11563983_15openalex-snapshot
Support Vector Inductive Logic Programming
inferredcites →https://doi.org/10.1007/bf02295996openalex-snapshot
Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages
inferredcites →https://doi.org/10.1007/s10822-007-9113-3openalex-snapshot
Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds
inferredcites →https://doi.org/10.1016/0743-1066(94)90035-3openalex-snapshot
Inductive Logic Programming: Theory and methods
inferredcites →https://doi.org/10.1016/j.bmc.2006.05.017openalex-snapshot
Molecular docking study and development of an empirical binding free energy model for phosphodiesterase 4 inhibitors
inferredcites →https://doi.org/10.1016/j.jmgm.2004.11.007openalex-snapshot
LigScore: a novel scoring function for predicting binding affinities
inferredcites →https://doi.org/10.1016/s0022-2836(03)00021-4openalex-snapshot
An Orientation-dependent Hydrogen Bonding Potential Improves Prediction of Specificity and Structure for Proteins and Protein–Protein Complexes
inferredcites →https://doi.org/10.1016/s0968-0004(00)89080-5openalex-snapshot
RASMOL: biomolecular graphics for all
inferredcites →https://doi.org/10.1016/s1093-3263(01)00125-5openalex-snapshot
Consensus scoring for ligand/protein interactions
inferredcites →https://doi.org/10.1021/bi972183gopenalex-snapshot
Structural Basis for Specificity of Retroviral Proteases
inferredcites →https://doi.org/10.1021/ci030006iopenalex-snapshot
BHB: A Simple Knowledge-Based Scoring Function to Improve the Efficiency of Database Screening
inferredcites →https://doi.org/10.1021/ci034289qopenalex-snapshot
Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
inferredcites →https://doi.org/10.1021/ci049870gopenalex-snapshot
Expanded Interaction Fingerprint Method for Analyzing Ligand Binding Modes in Docking and Structure-Based Drug Design
inferredcites →https://doi.org/10.1021/ci0498719openalex-snapshot
Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance
inferredcites →https://doi.org/10.1021/ci050296yopenalex-snapshot
The Use of Consensus Scoring in Ligand-Based Virtual Screening
inferredcites →https://doi.org/10.1021/ci050420dopenalex-snapshot
Knowledge-Based Interaction Fingerprint Scoring: A Simple Method for Improving the Effectiveness of Fast Scoring Functions
inferredcites →https://doi.org/10.1021/ci600223dopenalex-snapshot
A Novel Logic-Based Approach for Quantitative Toxicology Prediction
inferredcites →https://doi.org/10.1021/ja00226a005openalex-snapshot
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
inferredcites →https://doi.org/10.1021/ja002627nopenalex-snapshot
Contribution of Fluorine to Protein−Ligand Affinity in the Binding of Fluoroaromatic Inhibitors to Carbonic Anhydrase II
inferredcites →https://doi.org/10.1021/ja011034popenalex-snapshot
Fluoroaromatic−Fluoroaromatic Interactions between Inhibitors Bound in the Crystal Lattice of Human Carbonic Anhydrase II
inferredcites →https://doi.org/10.1021/ja026939xopenalex-snapshot
HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information
inferredcites →https://doi.org/10.1021/ja963832zopenalex-snapshot
Novel Binding Mode of Hydroxamate Inhibitors to Human Carbonic Anhydrase II
inferredcites →https://doi.org/10.1021/jm00050a010openalex-snapshot
Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity
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/jm010163dopenalex-snapshot
Structural Aspects of Isozyme Selectivity in the Binding of Inhibitors to Carbonic Anhydrases II and IV
inferredcites →https://doi.org/10.1021/jm030912mopenalex-snapshot
Unexpected Nanomolar Inhibition of Carbonic Anhydrase by COX-2-Selective Celecoxib: New Pharmacological Opportunities Due to Related Binding Site Recognition
inferredcites →https://doi.org/10.1021/jm050333copenalex-snapshot
Carbonic Anhydrase Inhibitors: Stacking with Phe131 Determines Active Site Binding Region of Inhibitors As Exemplified by the X-ray Crystal Structure of a Membrane-Impermeant Antitumor Sulfonamide Complexed with Isozyme II
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/jm050543popenalex-snapshot
Modeling Water Molecules in Protein−Ligand Docking Using GOLD
inferredcites →https://doi.org/10.1021/jm9602571openalex-snapshot
Cyclic HIV Protease Inhibitors: Synthesis, Conformational Analysis, P2/P2‘ Structure−Activity Relationship, and Molecular Recognition of Cyclic Ureas
inferredcites →https://doi.org/10.1021/jm960586topenalex-snapshot
Cyclic Urea Amides: HIV-1 Protease Inhibitors with Low Nanomolar Potency against both Wild Type and Protease Inhibitor Resistant Mutants of HIV
inferredcites →https://doi.org/10.1021/jm960588dopenalex-snapshot
Unexpected Binding Mode of a Cyclic Sulfamide HIV-1 Protease Inhibitor
inferredcites →https://doi.org/10.1021/jm981062ropenalex-snapshot
Three-Dimensional Quantitative Structure−Activity Relationship Analyses Using Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis To Elucidate Selectivity Differences of Inhibitors Binding to Trypsin, Thrombin, and Factor Xa
inferredcites →https://doi.org/10.1021/jm990352kopenalex-snapshot
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:1007460424845openalex-snapshot
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
inferredcites →https://doi.org/10.1023/a:1020155510718openalex-snapshot
A review of protein-small molecule docking methods
inferredcites →https://doi.org/10.1023/b:jcam.0000005748.19093.e8openalex-snapshot
Inhibition and substrate recognition – a computational approach applied to HIV protease
inferredcites →https://doi.org/10.1073/pnas.032673399openalex-snapshot
Combinatorial computational method gives new picomolar ligands for a known enzyme
inferredcites →https://doi.org/10.1073/pnas.89.23.11322openalex-snapshot
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
inferredcites →https://doi.org/10.1073/pnas.93.1.438openalex-snapshot
Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.
inferredcites →https://doi.org/10.1093/protein/8.2.127openalex-snapshot
LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions
inferredcites →https://doi.org/10.1186/1471-2105-6-122openalex-snapshot
SuperLigands – a database of ligand structures derived from the Protein Data Bank
inferredcites →https://doi.org/10.1529/biophysj.104.058768openalex-snapshot
Optimal Clustering for Detecting Near-Native Conformations in Protein Docking
inferredcites →https://doi.org/10.2174/1568026043451104openalex-snapshot
Guided Docking Approaches to Structure-Based Design and Screening
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Recent Advances in Drug Design With Machine Learning
inferred← citeshttps://doi.org/10.1002/cem.2503openalex-snapshot
Compressed images for affinity prediction (CIFAP): a study on predicting binding affinities for checkpoint kinase 1 protein inhibitors
inferred← citeshttps://doi.org/10.1002/jmr.2642openalex-snapshot
An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide
inferred← citeshttps://doi.org/10.1002/prot.22081openalex-snapshot
In pursuit of virtual lead optimization: The role of the receptor structure and ensembles in accurate docking
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Automated site preparation in physics‐based rescoring of receptor ligand complexes
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Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
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From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
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Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression
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A Counting-Based Heuristic for ILP-Based Concept Discovery Systems
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Complementarity of Hydrophobic/Hydrophilic Properties In Protein—Ligand Complexes: A New Tool to Improve Docking Results
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Improving molecular docking through eHiTS’ tunable scoring function
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Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S
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Molecular docking—A solved problem?
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Machine learning in computational docking
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Deffini: A family-specific deep neural network model for structure-based virtual screening
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Usability study of clinical exome analysis software: Top lessons learned and recommendations
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Benchmarking the robustness of the correct identification of flexible 3D objects using common machine learning models
inferred← citeshttps://doi.org/10.1021/ci400120bopenalex-snapshot
SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes
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Assessment of a Rule-Based Virtual Screening Technology (INDDEx) on a Benchmark Data Set
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DeltaDelta neural networks for lead optimization of small molecule potency
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
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Discovering rules for protein-ligand specificity using support vector inductive logic programming
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A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction
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Molecular docking: The role of noncovalent interactions in the formation of protein-nucleotide and protein-peptide complexes
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Open Babel: An open chemical toolbox
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Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
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Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4
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Support vector machines for drug discovery
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Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein–ligand interactions based on a study on caspase 3 inhibitors
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Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes
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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations
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