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StructRank: A New Approach for Ligand-Based Virtual Screening

https://doi.org/10.1021/ci100308f
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/cmdc.200900469openalex-snapshot
From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ
inferredcites →https://doi.org/10.1002/qsar.19840030402openalex-snapshot
Multivariate structure‐activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method
inferredcites →https://doi.org/10.1002/qsar.200430925openalex-snapshot
Prediction of Human Cytochrome P450 Inhibition Using Support Vector Machines
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/s10822-007-9125-zopenalex-snapshot
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
inferredcites →https://doi.org/10.1007/s10822-007-9160-9openalex-snapshot
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
inferredcites →https://doi.org/10.1007/s10822-008-9170-2openalex-snapshot
What do we know and when do we know it?
inferredcites →https://doi.org/10.1016/s0169-409x(96)00423-1openalex-snapshot
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1016/s1359-6446(02)02483-2openalex-snapshot
Structure-based virtual screening: an overview
inferredcites →https://doi.org/10.1016/s1359-6446(04)02939-3openalex-snapshot
Novel technologies for virtual screening
inferredcites →https://doi.org/10.1016/s1359-6446(97)01163-xopenalex-snapshot
Virtual screening—an overview
inferredcites →https://doi.org/10.1021/ci0100144openalex-snapshot
Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches
inferredcites →https://doi.org/10.1021/ci0340355openalex-snapshot
QSAR Study of Ethyl 2-[(3-Methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: An Inhibitor of AP-1 and NF-κB Mediated Gene Expression Based on Support Vector Machines
inferredcites →https://doi.org/10.1021/ci0341161openalex-snapshot
Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
inferredcites →https://doi.org/10.1021/ci034143ropenalex-snapshot
Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci049965iopenalex-snapshot
Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression
inferredcites →https://doi.org/10.1021/ci600205gopenalex-snapshot
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
inferredcites →https://doi.org/10.1021/ci600426eopenalex-snapshot
Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
inferredcites →https://doi.org/10.1021/ci800441copenalex-snapshot
Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors
inferredcites →https://doi.org/10.1021/ci9003865openalex-snapshot
Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
inferredcites →https://doi.org/10.1021/ja01062a035openalex-snapshot
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
inferredcites →https://doi.org/10.1021/jm000375vopenalex-snapshot
Improved Scoring of Ligand−Protein Interactions Using OWFEG Free Energy Grids
inferredcites →https://doi.org/10.1021/jm990408aopenalex-snapshot
Successful Virtual Screening of a Chemical Database for Farnesyltransferase Inhibitor Leads
inferredcites →https://doi.org/10.1021/mp0700413openalex-snapshot
Machine Learning Models for Lipophilicity and Their Domain of Applicability
inferredcites →https://doi.org/10.1038/nature03197openalex-snapshot
Virtual screening of chemical libraries
inferredcites →https://doi.org/10.1049/cp:19991091openalex-snapshot
Support vector learning for ordinal regression
inferredcites →https://doi.org/10.1056/nejme058038openalex-snapshot
COX-2 Inhibitors — A Lesson in Unexpected Problems
inferredcites →https://doi.org/10.1073/pnas.88.7.2692openalex-snapshot
Expression of a mitogen-responsive gene encoding prostaglandin synthase is regulated by mRNA splicing.
inferredcites →https://doi.org/10.1089/cmb.2007.0152openalex-snapshot
Support Vector Training of Protein Alignment Models
inferredcites →https://doi.org/10.1109/72.788641openalex-snapshot
Input space versus feature space in kernel-based methods
inferredcites →https://doi.org/10.1136/jnnp.52.8.1021-aopenalex-snapshot
Basic Neurochemistry: Molecular, Cellular, and Medical Aspects
inferredcites →https://doi.org/10.1145/1143844.1143863openalex-snapshot
Semi-supervised learning for structured output variables
inferredcites →https://doi.org/10.1145/1277741.1277790openalex-snapshot
A support vector method for optimizing average precision
inferredcites →https://doi.org/10.1145/3130348.3130374openalex-snapshot
IR evaluation methods for retrieving highly relevant documents
inferredcites →https://doi.org/10.1145/775047.775067openalex-snapshot
Optimizing search engines using clickthrough data
inferredcites →https://doi.org/10.1147/sj.402.0360openalex-snapshot
Large-scale virtual screening for discovering leads in the postgenomic era
inferredcites →https://doi.org/10.1162/neco.2007.19.5.1155openalex-snapshot
Training a Support Vector Machine in the Primal
inferredcites →https://doi.org/10.1172/jci111151openalex-snapshot
Concentrations of prostaglandin endoperoxide synthase and prostaglandin I2 synthase in the endothelium and smooth muscle of bovine aorta.
inferredcites →https://doi.org/10.1200/jco.1993.11.1.5openalex-snapshot
Karnofsky memorial lecture. Ode to methotrexate.
inferredcites →https://doi.org/10.2174/1381612053507549openalex-snapshot
New Methodologies for Ligand-Based Virtual Screening
inferredcites →https://doi.org/10.2174/138620706776055539openalex-snapshot
Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
inferredcites →https://doi.org/10.2174/138620709788167980openalex-snapshot
Machine Learning in Virtual Screening
inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot
Advances in Neural Information Processing Systems 14

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9781118742785.ch15openalex-snapshot
MOLECULAR SIMILARITY ANALYSIS
inferred← citeshttps://doi.org/10.1002/minf.201100059openalex-snapshot
Visual Interpretation of Kernel‐Based Prediction Models
inferred← citeshttps://doi.org/10.1007/978-3-030-33582-3_25openalex-snapshot
Methods to Improve Ranking Chemical Structures in Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1007/978-3-031-45275-8_19openalex-snapshot
Extrapolation is Not the Same as Interpolation
inferred← citeshttps://doi.org/10.1007/s10015-017-0416-8openalex-snapshot
PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM
inferred← citeshttps://doi.org/10.1007/s10822-016-0003-4openalex-snapshot
Quantum probability ranking principle for ligand-based virtual screening
inferred← citeshttps://doi.org/10.1007/s10994-024-06591-2openalex-snapshot
Extrapolation is not the same as interpolation
inferred← citeshttps://doi.org/10.1016/b978-0-12-822468-7.00005-5openalex-snapshot
Ontology-based intelligent decision support systems: A systematic approach
inferred← citeshttps://doi.org/10.1016/j.bmc.2012.03.030openalex-snapshot
Chemoinformatics: A view of the field and current trends in method development
inferred← citeshttps://doi.org/10.1016/j.cmpb.2014.08.009openalex-snapshot
Drug/nondrug classification using Support Vector Machines with various feature selection strategies
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2020.103660openalex-snapshot
Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
Machine-learning approaches in drug discovery: methods and applications
inferred← citeshttps://doi.org/10.1016/j.jmgm.2019.07.009openalex-snapshot
Learning-to-rank technique based on ignoring meaningless ranking orders between compounds
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00175openalex-snapshot
Visualization and Interpretation of Support Vector Machine Activity Predictions
inferred← citeshttps://doi.org/10.1021/acsomega.1c00463openalex-snapshot
Ranking-Oriented Quantitative Structure–Activity Relationship Modeling Combined with Assay-Wise Data Integration
inferred← citeshttps://doi.org/10.1021/ci200409xopenalex-snapshot
Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
inferred← citeshttps://doi.org/10.1021/ci300146hopenalex-snapshot
GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design
inferred← citeshttps://doi.org/10.1093/bib/bbaa394openalex-snapshot
Application of learning to rank in bioinformatics tasks
inferred← citeshttps://doi.org/10.1093/bioinformatics/btw244openalex-snapshot
DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank
inferred← citeshttps://doi.org/10.1109/cibcb55180.2022.9863032openalex-snapshot
Compound Virtual Screening by Learning-to-Rank with Gradient Boosting Decision Tree and Enrichment-based Cumulative Gain
inferred← citeshttps://doi.org/10.1109/tcbb.2014.2338308openalex-snapshot
Identification of Functionally Related Enzymes by Learning-to-Rank Methods
inferred← citeshttps://doi.org/10.1111/cbdd.12294openalex-snapshot
Modeling of Compound Profiling Experiments Using Support Vector Machines
inferred← citeshttps://doi.org/10.1186/s13321-014-0050-6openalex-snapshot
A ranking method for the concurrent learning of compounds with various activity profiles
inferred← citeshttps://doi.org/10.1186/s13321-015-0052-zopenalex-snapshot
When drug discovery meets web search: Learning to Rank for ligand-based virtual screening
inferred← citeshttps://doi.org/10.1371/journal.pone.0124600openalex-snapshot
MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development
inferred← citeshttps://doi.org/10.14569/ijacsa.2018.090235openalex-snapshot
Machine Learning Method To Screen Inhibitors of Virulent Transcription Regulator of Salmonella Typhi
inferred← citeshttps://doi.org/10.1517/17460441.2014.866943openalex-snapshot
Support vector machines for drug discovery
inferred← citeshttps://doi.org/10.2174/0115672050386924250930184405openalex-snapshot
Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer's Disease
inferred← citeshttps://doi.org/10.2197/ipsjtbio.11.41openalex-snapshot
Predicting Strategies for Lead Optimization via Learning to Rank
inferred← citeshttps://doi.org/10.3390/genes11020131openalex-snapshot
The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions
inferred← citeshttps://doi.org/10.3390/ph17050620openalex-snapshot
The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods
inferred← citeshttps://doi.org/10.64898/2026.06.22.733866openalex-snapshot
Real-world results from a Machine Learning-guided, phenotypic High-Throughput Screen for novel antibiotics
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