On the public citation graph

Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors

0 signed · 110 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1002/(sici)1099-1352(199601)9:1<1::aid-jmr241>3.0.co;2-6openalex-snapshot
Automated docking of flexible ligands: Applications of autodock
inferredcites →https://doi.org/10.1002/3527603743.ch9openalex-snapshot
WOMBAT: World of Molecular Bioactivity
inferredcites →https://doi.org/10.1002/bip.10207openalex-snapshot
Gaussian docking functions
inferredcites →https://doi.org/10.1002/jcc.20681openalex-snapshot
Ultrafast shape recognition to search compound databases for similar molecular shapes
inferredcites →https://doi.org/10.1006/jmbi.1996.0477openalex-snapshot
A Fast Flexible Docking Method using an Incremental Construction Algorithm
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/10.1007/bf00124499openalex-snapshot
Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4
inferredcites →https://doi.org/10.1016/j.drudis.2006.10.005openalex-snapshot
Similarity-based virtual screening using 2D fingerprints
inferredcites →https://doi.org/10.1021/ci00057a005openalex-snapshot
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
inferredcites →https://doi.org/10.1021/ci00062a008openalex-snapshot
SMILES. 2. Algorithm for generation of unique SMILES notation
inferredcites →https://doi.org/10.1021/ci00062a009openalex-snapshot
Computer translation of IUPAC systematic organic chemical nomenclature. 1. Introduction and background to a grammar-based approach
inferredcites →https://doi.org/10.1021/ci010132ropenalex-snapshot
Reoptimization of MDL Keys for Use in Drug Discovery
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/ci025605oopenalex-snapshot
CLIP: Similarity Searching of 3D Databases Using Clique Detection
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/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci034231bopenalex-snapshot
Comparison of Fingerprint-Based Methods for Virtual Screening Using Multiple Bioactive Reference Structures
inferredcites →https://doi.org/10.1021/ci0342876openalex-snapshot
SVM-Based Feature Selection for Characterization of Focused Compound Collections
inferredcites →https://doi.org/10.1021/ci049652nopenalex-snapshot
Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data
inferredcites →https://doi.org/10.1021/ci0496797openalex-snapshot
LINGO, an Efficient Holographic Text Based Method To Calculate Biophysical Properties and Intermolecular Similarities
inferredcites →https://doi.org/10.1021/ci049737oopenalex-snapshot
Classifying ‘Drug-likeness' with Kernel-Based Learning Methods
inferredcites →https://doi.org/10.1021/ci0503863openalex-snapshot
Similarity Searching in Databases of Flexible 3D Structures Using Autocorrelation Vectors Derived from Smoothed Bounded Distance Matrices
inferredcites →https://doi.org/10.1021/ci0600657openalex-snapshot
Applications of Self-Organizing Neural Networks in Virtual Screening and Diversity Selection
inferredcites →https://doi.org/10.1021/ci060104eopenalex-snapshot
Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks
inferredcites →https://doi.org/10.1021/ci600267kopenalex-snapshot
Synergistic Use of Compound Properties and Docking Scores in Neural Network Modeling of CYP2D6 Binding: Predicting Affinity and Conformational Sampling
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/ci700052xopenalex-snapshot
Comparison of Topological, Shape, and Docking Methods in Virtual Screening
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/jm049254bopenalex-snapshot
Greater Than the Sum of Its Parts: Combining Models for Useful ADMET Prediction
inferredcites →https://doi.org/10.1021/jm049611iopenalex-snapshot
Molecular Surface Point Environments for Virtual Screening and the Elucidation of Binding Patterns (MOLPRINT 3D)
inferredcites →https://doi.org/10.1021/jm0582165openalex-snapshot
Searching Techniques for Databases of Two- and Three-Dimensional Chemical Structures
inferredcites →https://doi.org/10.1021/jm0603365openalex-snapshot
Comparison of Shape-Matching and Docking as Virtual Screening Tools
inferredcites →https://doi.org/10.1023/a:1007934413448openalex-snapshot
IsoStar: A library of information about nonbonded interactions
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:1020155510718openalex-snapshot
A review of protein-small molecule docking methods
inferredcites →https://doi.org/10.1039/b409813gopenalex-snapshot
Molecular similarity: a key technique in molecular informatics
inferredcites →https://doi.org/10.1039/b409865jopenalex-snapshot
Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures
inferredcites →https://doi.org/10.1177/1087057105281048openalex-snapshot
Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9780470567623.ch4openalex-snapshot
Data Mining Approaches for Compound Selection and Iterative Screening
inferred← citeshttps://doi.org/10.1002/9781118742785.ch7openalex-snapshot
CHEMOMETRIC APPLICATIONS OF NAÏVE BAYESIAN MODELS IN DRUG DISCOVERY
inferred← citeshttps://doi.org/10.1002/9783527645121.ch8openalex-snapshot
Statistical Methods for Predicting Compound Recovery Rates for Ligand‐Based Virtual Screening and Assessing the Probability of Activity
inferred← citeshttps://doi.org/10.1002/jat.4331openalex-snapshot
In silico prediction of potential drug‐induced nephrotoxicity with machine learning methods
inferred← citeshttps://doi.org/10.1002/minf.201100069openalex-snapshot
Selecting Relevant Descriptors for Classification by Bayesian Estimates: A Comparison with Decision Trees and Support Vector Machines Approaches for Disparate Data Sets
inferred← citeshttps://doi.org/10.1002/minf.201400127openalex-snapshot
In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods
inferred← citeshttps://doi.org/10.1002/wcms.1096openalex-snapshot
Hansch analysis 50 years on
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_135openalex-snapshot
Drug Design with Machine Learning
inferred← citeshttps://doi.org/10.1007/978-1-4939-7717-8_15openalex-snapshot
Machine Learning-Based Modeling of Drug Toxicity
inferred← citeshttps://doi.org/10.1007/978-1-4939-7899-1_1openalex-snapshot
Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
inferred← citeshttps://doi.org/10.1007/978-1-4939-8639-2_5openalex-snapshot
Impact of Molecular Descriptors on Computational Models
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_11openalex-snapshot
Pharmacophore-Based Virtual Screening
inferred← citeshttps://doi.org/10.1007/s10822-016-9915-2openalex-snapshot
TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models
inferred← citeshttps://doi.org/10.1007/s10822-018-0155-5openalex-snapshot
Rescoring of docking poses under Occam’s Razor: are there simpler solutions?
inferred← citeshttps://doi.org/10.1016/b978-0-443-18638-7.00022-0openalex-snapshot
Chemical space and molecular descriptors for QSAR studies
inferred← citeshttps://doi.org/10.1016/b978-0-443-33032-2.00004-6openalex-snapshot
Application of artificial intelligence for drug discovery
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110695openalex-snapshot
In silico screening of a designed focused chemical space identifies novel alkyl hydrazides as potent HDAC11 inhibitors
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.ecoenv.2014.08.026openalex-snapshot
Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2019.02.014openalex-snapshot
Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2019.109822openalex-snapshot
Prediction on the mutagenicity of nitroaromatic compounds using quantum chemistry descriptors based QSAR and machine learning derived classification methods
inferred← citeshttps://doi.org/10.1016/j.heliyon.2022.e10495openalex-snapshot
Multi-stage virtual screening of natural products against p38α mitogen-activated protein kinase: predictive modeling by machine learning, docking study and molecular dynamics simulation
inferred← citeshttps://doi.org/10.1016/j.tiv.2019.01.004openalex-snapshot
Prediction of the skin sensitising potential and potency of compounds via mechanism-based binary and ternary classification models
inferred← citeshttps://doi.org/10.1016/j.tiv.2021.105089openalex-snapshot
In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods
inferred← citeshttps://doi.org/10.1016/j.ymeth.2014.08.010openalex-snapshot
Virtual screening strategies: Recent advances in the identification and design of anti-cancer agents
inferred← citeshttps://doi.org/10.1016/s1574-1400(08)00011-xopenalex-snapshot
SAR Knowledge Bases in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.7b00037openalex-snapshot
In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.8b00162openalex-snapshot
Prediction of Farnesoid X Receptor Disruptors with Machine Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00447openalex-snapshot
In Silico Prediction of Human Renal Clearance of Compounds Using Quantitative Structure-Pharmacokinetic Relationship Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00290openalex-snapshot
Identification of Bioisosteric Substituents by a Deep Neural Network
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00476openalex-snapshot
Insights into the Molecular Basis of the Acute Contact Toxicity of Diverse Organic Chemicals in the Honey Bee
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00656openalex-snapshot
Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods
inferred← citeshttps://doi.org/10.1021/ci200028nopenalex-snapshot
Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers
inferred← citeshttps://doi.org/10.1021/ci200622dopenalex-snapshot
In Silico Assessment of Chemical Biodegradability
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/ci4002712openalex-snapshot
Comparison of Confirmed Inactive and Randomly Selected Compounds as Negative Training Examples in Support Vector Machine-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci5000467openalex-snapshot
In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods
inferred← citeshttps://doi.org/10.1021/ci500253qopenalex-snapshot
Discovering New Agents Active against Methicillin-Resistant Staphylococcus aureus with Ligand-Based Approaches
inferred← citeshttps://doi.org/10.1021/ci500698aopenalex-snapshot
Expert System for Predicting Reaction Conditions: The Michael Reaction Case
inferred← citeshttps://doi.org/10.1021/ci900035zopenalex-snapshot
Mining Statistically Significant Molecular Substructures for Efficient Molecular Classification
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/c4tx00174eopenalex-snapshot
In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts
inferred← citeshttps://doi.org/10.1039/c5md00149hopenalex-snapshot
Identifying farnesoid X receptor agonists by naïve Bayesian and recursive partitioning approaches
inferred← citeshttps://doi.org/10.1039/c5ra23289aopenalex-snapshot
Chemical fragment-based CDK4/6 inhibitors prediction and web server
inferred← citeshttps://doi.org/10.1039/c7md00074jopenalex-snapshot
In silico prediction of chemical subcellular localization via multi-classification methods
inferred← citeshttps://doi.org/10.1039/c7ra08415copenalex-snapshot
In silico prediction of chronic toxicity with chemical category approaches
inferred← citeshttps://doi.org/10.1039/c7ra12957bopenalex-snapshot
The development and application of in silico models for drug induced liver injury
inferred← citeshttps://doi.org/10.1039/c7tx00144dopenalex-snapshot
In silico prediction of pesticide aquatic toxicity with chemical category approaches
inferred← citeshttps://doi.org/10.1039/c8tx00331aopenalex-snapshot
In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning
inferred← citeshttps://doi.org/10.1051/medsci/201834f110openalex-snapshot
Computational QSAR model combined molecular descriptors and fingerprints to predict HDAC1 inhibitors
inferred← citeshttps://doi.org/10.1101/798561openalex-snapshot
KinasepKipred: A Predictive Model for Estimating Ligand-Kinase Inhibitor Constant ( pK i )
inferred← citeshttps://doi.org/10.1109/ijcnn60899.2024.10650746openalex-snapshot
Molecular Classification Using Hyperdimensional Graph Classification
inferred← citeshttps://doi.org/10.1136/amiajnl-2013-002512openalex-snapshot
Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
inferred← citeshttps://doi.org/10.1186/s12859-023-05455-1openalex-snapshot
Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features
inferred← citeshttps://doi.org/10.1186/s13321-019-0389-9openalex-snapshot
A multiple classifier system identifies novel cannabinoid CB2 receptor ligands
inferred← citeshttps://doi.org/10.1371/journal.pone.0095221openalex-snapshot
Predicting mTOR Inhibitors with a Classifier Using Recursive Partitioning and Naïve Bayesian Approaches
inferred← citeshttps://doi.org/10.1517/17460441.2010.486830openalex-snapshot
Advances in 2D fingerprint similarity searching
inferred← citeshttps://doi.org/10.2174/1381612824666180607124038openalex-snapshot
Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review
inferred← citeshttps://doi.org/10.2174/1381612827666210622104428openalex-snapshot
Application of Machine Learning Methods for the Development of Antidiabetic Drugs
inferred← citeshttps://doi.org/10.26434/chemrxiv.13525457openalex-snapshot
Identification of Bioisosteric Scaffolds using Scaffold Keys
inferred← citeshttps://doi.org/10.26434/chemrxiv.13525457.v1openalex-snapshot
Identification of Bioisosteric Scaffolds using Scaffold Keys
inferred← citeshttps://doi.org/10.3389/fgene.2020.01000openalex-snapshot
A Machine Learning Method for Drug Combination Prediction
inferred← citeshttps://doi.org/10.3389/fmolb.2020.556481openalex-snapshot
Discovery of Potential Flavonoid Inhibitors Against COVID-19 3CL Proteinase Based on Virtual Screening Strategy
inferred← citeshttps://doi.org/10.3390/molecules23112892openalex-snapshot
In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods
inferred← citeshttps://doi.org/10.3390/pharmaceutics14050943openalex-snapshot
Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
inferred← citeshttps://doi.org/10.3390/pr9112074openalex-snapshot
Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors
inferred← citeshttps://doi.org/10.3797/scipharm.0802-05openalex-snapshot
The Similarity Principle – New Trends and Applications in Ligand-Based Drug Discovery and ADMET Profiling
inferred← citeshttps://doi.org/10.4155/9781909453012openalex-snapshot
In Silico Drug Discovery and Design
inferred← citeshttps://doi.org/10.4155/ebo.12.419openalex-snapshot
Machine learning and similarity-based virtual screening techniques
inferred← citeshttps://doi.org/10.70675/6da007edz02eaz4e97z8d1bzd4ff2baa1336openalex-snapshot
Espaces chimiques optimaux pour la recherche par similarité, la classification et la modélisation de réactions chimiques représentées par des graphes condensés de réactions
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.