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Quantitative Structure−Activity Relationship Modeling of Juvenile Hormone Mimetic Compounds forCulexPipiensLarvae, with a Discussion of Descriptor-Thinning Methods
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Handbook of Molecular Descriptors
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Generalization of pair correlation method (PCM) for non‐parametric variable selection
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Molecular Connectivity in Structure–activity Analysis
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Correlation Analysis in Chemistry
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Finding subsets which fit well
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A new approach for devising local graph invariants: Derived topological indices with low degeneracy and good correlation ability
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Highly discriminating distance-based topological index
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Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology
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Predicting blood:air partition coefficients using theoretical molecular descriptors
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Atom pairs as molecular features in structure-activity studies: definition and applications
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Computer code for producing Eh-pH plots of equilibrium chemical systems
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On the Physical Interpretation of QSAR Models
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Quantum-Chemical Descriptors in QSAR/QSPR Studies
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Characterization of molecular branching
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Importance of nonbonded attraction in the stereochemistry of the SN2' reaction
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Raman Spectra of Aqueous Solutions of Potassium Thiocyanate
inferredcites →https://doi.org/10.1021/jf00086a042openalex-snapshot
Development of (phenoxyphenoxy)- and (benzylphenoxy)propyl ethers as potent insect juvenile hormone mimetics
inferredcites →https://doi.org/10.1021/jf00086a043openalex-snapshot
Development of (4-alkoxyphenoxy)- and (4-alkylphenoxy)alkanaldoxime O-ethers as potent insect juvenile hormone mimics and their structure-activity relationships
inferredcites →https://doi.org/10.1021/jf00092a040openalex-snapshot
Development of N,O-disubstituted hydroxylamines and N,N-disubstituted amines as insect juvenile hormone mimetics and the role of the nitrogenous function for activity
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Information theory, distance matrix, and molecular branching
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Influence Measures in Ridge Regression
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Discussion: PLS in Chemical Practice
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Principal Components Regression in Exploratory Statistical Research
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Linear Model Selection by Cross-Validation
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Variable selection using pair-correlation method. Environmental applications
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Prediction of Human Blood: Air Partition Coefficient: A Comparison of Structure‐Based and Property‐Based Methods
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Regression Shrinkage and Selection Via the Lasso
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Subset Selection in Regression
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Quantitative Structure-Activity Relationship (QSAR) Models of Mutagens and Carcinogens
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Elements of Statistical Computing
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Topological Indices and Related Descriptors in QSAR and QSPR
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Applied Regression Analysis (2nd ed).
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Quantitative Structure–Activity Relationship (QSAR) Modeling of Human Blood : Air Partitioning with Proper Statistical Methods and Validation
inferred← citeshttps://doi.org/10.1002/cbdv.201000111openalex-snapshot
Characterization of Dihydrofolate Reductases from Multiple Strains of Plasmodium falciparum Using Mathematical Descriptors of Their Inhibitors
inferred← citeshttps://doi.org/10.1002/chin.200616217openalex-snapshot
Quantitative Structure—Activity Relationship Modeling of Juvenile Hormone Mimetic Compounds for Culex pipiens Larvae, with a Discussion of Descriptor‐Thinning Methods.
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Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood‐brain Barrier Entry of Chemical Compounds Using Machine Learning
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On the Relevance of Feature Selection Algorithms While Developing Non-linear QSARs
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Biological Activity and Toxicity: A Conceptual DFT Approach
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3D-QSAR studies on the biological activity of juvenile hormone mimetic compounds for Culex pipiens Larvae
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Quantitative structure-activity relationship studies of boron-containing dipeptide proteasome inhibitors using calculated mathematical descriptors
inferred← citeshttps://doi.org/10.1016/b978-0-12-391500-9.00005-xopenalex-snapshot
Recent Progress in Juvenile Hormone Analogs (JHA) Research
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Quantitative structure–retention relationship for the Kovats retention indices of a large set of terpenes: A combined data splitting-feature selection strategy
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Quantitative Structure–Activity Relationship (QSAR) modeling of juvenile hormone activity: Comparison of validation procedures
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Predicting biological activity: Computational approach using novel distance based molecular descriptors
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Selectivity-based QSAR approach for screening and evaluation of TRH analogs for TRH-R1 and TRH-R2 receptors subtypes
inferred← citeshttps://doi.org/10.1016/j.sciaf.2026.e03206openalex-snapshot
Machine Learning– driven discovery and optimization of PI3Kδ inhibitors for diffuse large B-cell lymphoma
inferred← citeshttps://doi.org/10.1021/ci100169popenalex-snapshot
Jackknife-Based Selection of Gram−Schmidt Orthogonalized Descriptors in QSAR
inferred← citeshttps://doi.org/10.1021/ie8018406openalex-snapshot
Quantitative Structure−Property Relationship (QSPR) Prediction of Liquid Viscosities of Pure Organic Compounds Employing Random Forest Regression
inferred← citeshttps://doi.org/10.1021/jm060526fopenalex-snapshot
Virtual Screening for Aryl Hydrocarbon Receptor Binding Prediction
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Predicting the vapour pressure of chemicals from structure: a comparison of graph theoretic versus quantum chemical descriptors
inferred← citeshttps://doi.org/10.1080/10629361003770951openalex-snapshot
Quantitative structure-activity relationships for cycloguanil analogs as PfDHFR inhibitors using mathematical molecular descriptors
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In silico models for predicting vector control chemicals targeting Aedes aegypti
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Predicting Chemical Reactivity and Bioactivity of Molecules from Structure
inferred← citeshttps://doi.org/10.1371/journal.pone.0006001openalex-snapshot
Molecular Determinants of Juvenile Hormone Action as Revealed by 3D QSAR Analysis in Drosophila
inferred← citeshttps://doi.org/10.21775/cimb.042.455openalex-snapshot
A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning
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Alignment-Free Method to Predict Enzyme Classes and Subclasses
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