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Optimization of molecular docking scores with support vector rank regression
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/jcc.21078openalex-snapshot
A novel method for protein‐ligand binding affinity prediction and the related descriptors exploration
inferredcites →https://doi.org/10.1002/jcc.21190openalex-snapshot
Prediction of human cytochrome P450 2B6‐substrate interactions using hierarchical support vector regression approach
inferredcites →https://doi.org/10.1002/jcc.21643openalex-snapshot
Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database
inferredcites →https://doi.org/10.1002/jcc.21936openalex-snapshot
A sequence‐based computational model for the prediction of the solvent accessible surface area for α‐helix and β‐barrel transmembrane residues
inferredcites →https://doi.org/10.1002/prot.10465openalex-snapshot
Improved protein–ligand docking using GOLD
inferredcites →https://doi.org/10.1002/prot.20088openalex-snapshot
A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
inferredcites →https://doi.org/10.1002/prot.20588openalex-snapshot
General and targeted statistical potentials for protein–ligand interactions
inferredcites →https://doi.org/10.1002/prot.23173openalex-snapshot
The interprotein scoring noises in glide docking scores
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/s10822-009-9317-9openalex-snapshot
Docking flexible ligands in proteins with a solvent exposure- and distance-dependent dielectric function
inferredcites →https://doi.org/10.1007/s11004-008-9156-6openalex-snapshot
An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing
inferredcites →https://doi.org/10.1016/j.cbpa.2007.08.033openalex-snapshot
Virtual screening strategies in drug discovery
inferredcites →https://doi.org/10.1016/s1359-6446(97)01163-xopenalex-snapshot
Virtual screening—an overview
inferredcites →https://doi.org/10.1016/s1367-5931(00)00217-9openalex-snapshot
High-throughput docking for lead generation
inferredcites →https://doi.org/10.1021/ci7000378openalex-snapshot
Evaluations of Molecular Docking Programs for Virtual Screening
inferredcites →https://doi.org/10.1021/ci800298zopenalex-snapshot
Empirical Scoring Functions for Advanced Protein−Ligand Docking with PLANTS
inferredcites →https://doi.org/10.1021/ci9004139openalex-snapshot
Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures
inferredcites →https://doi.org/10.1021/ja9039373openalex-snapshot
Importance of Ligand Reorganization Free Energy in Protein−Ligand Binding-Affinity Prediction
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/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/jm030644sopenalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
inferredcites →https://doi.org/10.1021/jm048957qopenalex-snapshot
The PDBbind Database: Methodologies and Updates
inferredcites →https://doi.org/10.1021/jm050362nopenalex-snapshot
A Critical Assessment of Docking Programs and Scoring Functions
inferredcites →https://doi.org/10.1021/jm050619hopenalex-snapshot
Extraction and Visualization of Potential Pharmacophore Points Using Support Vector Machines: Application to Ligand-Based Virtual Screening for COX-2 Inhibitors
inferredcites →https://doi.org/10.1021/jm070645aopenalex-snapshot
Targeting Plague Virulence Factors: A Combined Machine Learning Method and Multiple Conformational Virtual Screening for the Discovery of Yersinia Protein Kinase A Inhibitors
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/10.1038/nrd1549openalex-snapshot
Docking and scoring in virtual screening for drug discovery: methods and applications
inferredcites →https://doi.org/10.1038/nrd3139openalex-snapshot
Virtual screening: an endless staircase?
inferredcites →https://doi.org/10.1038/sj.bjp.0707515openalex-snapshot
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
inferredcites →https://doi.org/10.1080/1062936x.2011.569508openalex-snapshot
Androgen receptor binding affinity: a QSAR evaluation
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/10.1110/ps.0217002openalex-snapshot
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction
inferredcites →https://doi.org/10.1111/j.1582-4934.2008.00665.xopenalex-snapshot
Docking, virtual high throughput screening and in silico fragment‐based drug design
inferredcites →https://doi.org/10.2174/138920306778559412openalex-snapshot
eHiTS: An Innovative Approach to the Docking and Scoring Function Problems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/jcc.24667openalex-snapshot
Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest
inferred← citeshttps://doi.org/10.1002/jmr.2471openalex-snapshot
Improvements, trends, and new ideas in molecular docking: 2012–2013 in review
inferred← citeshttps://doi.org/10.1002/wcms.1225openalex-snapshot
Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
inferred← citeshttps://doi.org/10.1007/s10822-021-00423-4openalex-snapshot
Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2016.04.005openalex-snapshot
Machine learning optimization of cross docking accuracy
inferred← citeshttps://doi.org/10.1016/j.drudis.2024.103891openalex-snapshot
Drugging the entire human proteome: Are we there yet?
inferred← citeshttps://doi.org/10.1016/j.ejmech.2014.01.019openalex-snapshot
Integrating docking scores, interaction profiles and molecular descriptors to improve the accuracy of molecular docking: Toward the discovery of novel Akt1 inhibitors
inferred← citeshttps://doi.org/10.1016/j.engappai.2015.06.021openalex-snapshot
Comparative assessment of machine-learning scoring functions on PDBbind 2013
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00574openalex-snapshot
Toward a Computational Ecotoxicity Assay
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00418openalex-snapshot
Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review
inferred← citeshttps://doi.org/10.1038/srep40053openalex-snapshot
Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme
inferred← citeshttps://doi.org/10.1109/jac-ecc61002.2023.10479635openalex-snapshot
Deep Learning is Competing with Random Forest in Computational Docking
inferred← citeshttps://doi.org/10.3390/ijms20184574openalex-snapshot
Key Topics in Molecular Docking for Drug Design
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