On the public citation graph

Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors

https://doi.org/10.1021/ci800441c
0 signed · 76 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1016/j.drudis.2006.10.005openalex-snapshot
Similarity-based virtual screening using 2D fingerprints
inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot
Drug design by machine learning: support vector machines for pharmaceutical data analysis
inferredcites →https://doi.org/10.1021/ci025569topenalex-snapshot
Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins
inferredcites →https://doi.org/10.1021/ci025620topenalex-snapshot
Active Learning with Support Vector Machines in the Drug Discovery Process
inferredcites →https://doi.org/10.1021/ci034207yopenalex-snapshot
Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier
inferredcites →https://doi.org/10.1021/ci049641uopenalex-snapshot
Virtual Screening of Molecular Databases Using a Support Vector Machine
inferredcites →https://doi.org/10.1021/ci700461sopenalex-snapshot
Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds
inferredcites →https://doi.org/10.1021/ci950275bopenalex-snapshot
Chemical Similarity Using Geometric Atom Pair Descriptors
inferredcites →https://doi.org/10.1021/jm9806998openalex-snapshot
New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Applications: Overview of the Method and Applications, Including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1038/nature03196openalex-snapshot
Exploring biology with small organic molecules
inferredcites →https://doi.org/10.1038/nbt1228openalex-snapshot
Global mapping of pharmacological space
inferredcites →https://doi.org/10.1038/nrg1317openalex-snapshot
Chemogenomics: an emerging strategy for rapid target and drug discovery
inferredcites →https://doi.org/10.1039/b312875jopenalex-snapshot
Chemical genetics to chemical genomics: small molecules offer big insights
inferredcites →https://doi.org/10.1111/j.1747-0285.2007.00554.xopenalex-snapshot
Methods for Computer‐aided Chemical Biology. Part 1: Design of a Benchmark System for the Evaluation of Compound Selectivity
inferredcites →https://doi.org/10.1111/j.1747-0285.2007.00555.xopenalex-snapshot
Methods for Computer‐aided Chemical Biology. Part 2: Evaluation of Compound Selectivity Using 2D Molecular Fingerprints
inferredcites →https://doi.org/10.1111/j.1747-0285.2008.00670.xopenalex-snapshot
Methods for Computer‐Aided Chemical Biology. Part 3: Analysis of Structure–Selectivity Relationships through Single‐ or Dual‐Step Selectivity Searching and Bayesian Classification
inferredcites →https://doi.org/10.1145/130385.130401openalex-snapshot
A training algorithm for optimal margin classifiers
inferredcites →https://doi.org/10.1145/1557019openalex-snapshot
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
inferredcites →https://doi.org/10.1517/17460440802536496openalex-snapshot
Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery
inferredcites →https://doi.org/10.5555/299094openalex-snapshot
Advances in kernel methods: support vector learning

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9781118618110.ch15openalex-snapshot
Virtual Screening Methods
inferred← citeshttps://doi.org/10.1002/9783527633326.ch11openalex-snapshot
Applied Virtual Screening: Strategies, Recommendations, and Caveats
inferred← citeshttps://doi.org/10.1002/ddr.20407openalex-snapshot
Machine learning in drug discovery and development
inferred← citeshttps://doi.org/10.1002/minf.201000083openalex-snapshot
Multi‐class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods
inferred← citeshttps://doi.org/10.1002/minf.201100126openalex-snapshot
Linear and Nonlinear Support Vector Machine for the Classification of Human 5‐HT1A Ligand Functionality
inferred← citeshttps://doi.org/10.1007/978-1-4419-6045-0_19openalex-snapshot
Trends in Chemical Graph Data Mining
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_17openalex-snapshot
The Interweaving of Cheminformatics and HTS
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_20openalex-snapshot
Molecular Test Systems for Computational Selectivity Studies and Systematic Analysis of Compound Selectivity Profiles
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_21openalex-snapshot
Application of Support Vector Machine-Based Ranking Strategies to Search for Target-Selective Compounds
inferred← citeshttps://doi.org/10.1007/s00214-017-2083-1openalex-snapshot
Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies
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/s12206-012-0309-7openalex-snapshot
Fault diagnosis using binary tree and sphere-structured support vector machines
inferred← citeshttps://doi.org/10.1016/j.brainres.2010.03.095openalex-snapshot
Computational methodologies for studying non-coding RNAs relevant to central nervous system function and dysfunction
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.drudis.2022.07.004openalex-snapshot
Harnessing systematic protein–ligand interaction fingerprints for drug discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00552openalex-snapshot
Differential Compound Prioritization via Bidirectional Selectivity Push with Power
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b02041openalex-snapshot
Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach
inferred← citeshttps://doi.org/10.1021/ci100072zopenalex-snapshot
Pharmaceutical Perspectives of Nonlinear QSAR Strategies
inferred← citeshttps://doi.org/10.1021/ci100308fopenalex-snapshot
StructRank: A New Approach for Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci100399jopenalex-snapshot
Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS) and Its Application on Modeling Ligand Functionality for 5HT-Subtype GPCR Families
inferred← citeshttps://doi.org/10.1021/ci1004948openalex-snapshot
GPU Accelerated Chemical Similarity Calculation for Compound Library Comparison
inferred← citeshttps://doi.org/10.1021/ci200346bopenalex-snapshot
Improved Machine Learning Models for Predicting Selective Compounds
inferred← citeshttps://doi.org/10.1021/ci200607fopenalex-snapshot
Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors
inferred← citeshttps://doi.org/10.1021/ci3003914openalex-snapshot
LiCABEDS II. Modeling of Ligand Selectivity for G-Protein-Coupled Cannabinoid Receptors
inferred← citeshttps://doi.org/10.1021/ci400090topenalex-snapshot
Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations
inferred← citeshttps://doi.org/10.1021/ci4004909openalex-snapshot
Prediction of New Bioactive Molecules using a Bayesian Belief Network
inferred← citeshttps://doi.org/10.1021/ci9000376openalex-snapshot
Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods
inferred← citeshttps://doi.org/10.1021/ci9002624openalex-snapshot
Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects
inferred← citeshttps://doi.org/10.1021/ci9003865openalex-snapshot
Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
inferred← citeshttps://doi.org/10.1021/ci900419kopenalex-snapshot
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
inferred← citeshttps://doi.org/10.1039/c4mb00718bopenalex-snapshot
Predicting selective liver X receptor β agonists using multiple machine learning methods
inferred← citeshttps://doi.org/10.1039/c9ob01341eopenalex-snapshot
Phenotype-based discovery of a HeLa-specific cytotoxic molecule that downregulates HPV-mediated signaling pathways via oxidative damage
inferred← citeshttps://doi.org/10.1101/2022.07.10.499476openalex-snapshot
Machine learning approaches to quantitively predict selectivity of compounds against hDAC1 and hDAC6 isoforms
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/fskd.2017.8393148openalex-snapshot
CFXGBoost: Topic phrase extraction based on context features and XGBoost for knowledge base question answering
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2009.00848.xopenalex-snapshot
Methods for Computer‐Aided Chemical Biology. Part 5: Rationalizing the Selectivity of Cathepsin Inhibitors on the Basis of Molecular Fragments and Topological Feature Distributions
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2010.01059.xopenalex-snapshot
Potency‐Directed Similarity Searching Using Support Vector Machines
inferred← citeshttps://doi.org/10.11113/jt.v78.9245openalex-snapshot
AN ACTIVITY PREDICTION MODEL USING SHAPE-BASED DESCRIPTOR METHOD
inferred← citeshttps://doi.org/10.1137/1.9781611972818.72openalex-snapshot
The Infinite Push: A New Support Vector Ranking Algorithm that Directly Optimizes Accuracy at the Absolute Top of the List
inferred← citeshttps://doi.org/10.1142/s2737416524500121openalex-snapshot
Machine Learning Approaches to Predict the Selectivity of Compounds against HDAC1 and HDAC6
inferred← citeshttps://doi.org/10.1145/2147805.2147817openalex-snapshot
Improved machine learning models for predicting selective compounds
inferred← citeshttps://doi.org/10.1186/1758-2946-4-s1-p12openalex-snapshot
Potency-directed similarity searching using support vector machines
inferred← citeshttps://doi.org/10.1186/s12859-018-2093-6openalex-snapshot
Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis
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/10.1371/journal.pone.0156986openalex-snapshot
An Algorithm to Identify Target-Selective Ligands – A Case Study of 5-HT7/5-HT1A Receptor Selectivity
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/10.1517/17460441.2016.1117070openalex-snapshot
An overview of molecular fingerprint similarity search in virtual screening
inferred← citeshttps://doi.org/10.2174/1573409912666160219113250openalex-snapshot
Using Deep Learning for Compound Selectivity Prediction
inferred← citeshttps://doi.org/10.22159/ijap.2026v18i1.55744openalex-snapshot
THE AI REVOLUTION IN PHARMACEUTICALS: INNOVATIONS, CHALLENGES, AND FUTURE PROSPECTS – AN OVERVIEW
inferred← citeshttps://doi.org/10.3233/ida-160862openalex-snapshot
ToPoMine: A graph miner for analysis of atom-dynamics simulation data in material science
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/molecules21080983openalex-snapshot
Bioactive Molecule Prediction Using Extreme Gradient Boosting
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.