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On the Physical Interpretation of QSAR Models

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

inferredcites →https://doi.org/10.1002/0471725382openalex-snapshot
Robust Regression and Outlier Detection
inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/9783527616824openalex-snapshot
QSAR: Hansch Analysis and Related Approaches
inferredcites →https://doi.org/10.1002/cem.1180060605openalex-snapshot
Predictive ability of regression models. Part II: Selection of the best predictive PLS model
inferredcites →https://doi.org/10.1002/jcc.540130411openalex-snapshot
Atomic charge calculations for quantitative structure—property relationships
inferredcites →https://doi.org/10.1016/0160-9327(86)90116-xopenalex-snapshot
Molecular connectivity in structure-activity analysis
inferredcites →https://doi.org/10.1016/b978-0-12-406560-4.x5001-6openalex-snapshot
Molecular Connectivity in Chemistry and Drug Research
inferredcites →https://doi.org/10.1016/s0165-6147(00)89046-xopenalex-snapshot
QSAR: Hansch analysis and related approaches
inferredcites →https://doi.org/10.1021/ac00220a013openalex-snapshot
Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies
inferredcites →https://doi.org/10.1021/ac9705733openalex-snapshot
Theoretical Justification of Wavelength Selection in PLS Calibration: Development of a New Algorithm
inferredcites →https://doi.org/10.1021/ci60006a015openalex-snapshot
Generation of Descriptors from Molecular Structures
inferredcites →https://doi.org/10.1080/00401706.1989.10488524openalex-snapshot
Robust Regression and Outlier Detection
inferredcites →https://doi.org/10.1080/10629360290002811openalex-snapshot
Charged partial surface area (CPSA) descriptors QSAR applications

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

inferred← citeshttps://doi.org/10.1002/9783527628766.biblioopenalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1002/chin.200350234openalex-snapshot
Physical Interpretation of QSAR Models.
inferred← citeshttps://doi.org/10.1002/ddr.20044openalex-snapshot
Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents
inferred← citeshttps://doi.org/10.1002/jps.20985openalex-snapshot
Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
inferred← citeshttps://doi.org/10.1002/jssc.200301672openalex-snapshot
Elution and preliminary structure‐retention modeling of polar and ionic substances in supercritical fluid chromatography using volatile ammonium salts as mobile phase additives
inferred← citeshttps://doi.org/10.1002/minf.201300029openalex-snapshot
Universal Approach for Structural Interpretation of QSAR/QSPR Models
inferred← citeshttps://doi.org/10.1002/prot.21349openalex-snapshot
Amino acid sequence autocorrelation vectors and bayesian‐regularized genetic neural networks for modeling protein conformational stability: Gene V protein mutants
inferred← citeshttps://doi.org/10.1002/qsar.200510009openalex-snapshot
Data and Graph Mining in Chemical Space for ADME and Activity Data Sets
inferred← citeshttps://doi.org/10.1002/qsar.200610001openalex-snapshot
Modeling of the Inhibition Constant (Ki) of Some Cruzain Ketone‐Based Inhibitors Using 2D Spatial Autocorrelation Vectors and Data‐Diverse Ensembles of Bayesian‐Regularized Genetic Neural Networks
inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_8openalex-snapshot
Neural Networks in Building QSAR Models
inferred← citeshttps://doi.org/10.1007/978-1-62703-342-8_6openalex-snapshot
On Exploring Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1007/978-3-319-23419-9_10openalex-snapshot
The Procter and Gamble Company: Current State and Future Needs in Materials Modeling
inferred← citeshttps://doi.org/10.1007/s00204-005-0671-7openalex-snapshot
The uridine diphosphate glucuronosyltransferases: quantitative structure–activity relationships for hydroxyl polychlorinated biphenyl substrates
inferred← citeshttps://doi.org/10.1007/s00894-006-0163-6openalex-snapshot
QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties
inferred← citeshttps://doi.org/10.1007/s00894-008-0433-6openalex-snapshot
Modeling of peroxide activation in artemisinin derivatives by serial docking
inferred← citeshttps://doi.org/10.1007/s10822-008-9204-9openalex-snapshot
On the importance of topological descriptors in understanding structure–property relationships
inferred← citeshttps://doi.org/10.1007/s10822-008-9225-4openalex-snapshot
Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction
inferred← citeshttps://doi.org/10.1007/s10822-008-9240-5openalex-snapshot
On the interpretation and interpretability of quantitative structure–activity relationship models
inferred← citeshttps://doi.org/10.1007/s10822-019-00231-xopenalex-snapshot
www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets
inferred← citeshttps://doi.org/10.1007/s10822-021-00387-5openalex-snapshot
Development and interpretation of a QSAR model for in vitro breast cancer (MCF-7) cytotoxicity of 2-phenylacrylonitriles
inferred← citeshttps://doi.org/10.1016/b978-0-443-49144-3.00007-1openalex-snapshot
Unraveling the neuroprotective potential of bioactive natural products: Insights from structure–activity studies inAlzheimer's disease
inferred← citeshttps://doi.org/10.1016/j.bmc.2004.11.033openalex-snapshot
Understanding topoisomerase I and II in terms of QSAR
inferred← citeshttps://doi.org/10.1016/j.bmc.2005.05.002openalex-snapshot
An approach toward the problem of outliers in QSAR
inferred← citeshttps://doi.org/10.1016/j.bmc.2005.12.048openalex-snapshot
2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.06.014openalex-snapshot
QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-α-phenylsulfonylacetamide derivatives
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.06.050openalex-snapshot
Identification and characterization of novel sodium/potassium-ATPase inhibitors by virtual screening of a compound database
inferred← citeshttps://doi.org/10.1016/j.cbpa.2004.04.005openalex-snapshot
Applying data mining techniques to library design, lead generation and lead optimization
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.04.011openalex-snapshot
Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: Chymotrypsin inhibitor 2 mutants
inferred← citeshttps://doi.org/10.1016/j.jmgm.2014.10.006openalex-snapshot
3D-MoRSE descriptors explained
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00520openalex-snapshot
In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints
inferred← citeshttps://doi.org/10.1021/ci034158popenalex-snapshot
Development of a Quantitative Structure−Activity Relationship Model for Inhibition of Gram-positive Bacterial Cell Growth by Biarylamides
inferred← citeshttps://doi.org/10.1021/ci0342324openalex-snapshot
Feature Selection for Descriptor Based Classification Models. 1. Theory and GA-SEC Algorithm
inferred← citeshttps://doi.org/10.1021/ci034233wopenalex-snapshot
Feature Selection for Descriptor Based Classification Models. 2. Human Intestinal Absorption (HIA)
inferred← citeshttps://doi.org/10.1021/ci034284topenalex-snapshot
Development and Use of Hydrophobic Surface Area (HSA) Descriptors for Computer-Assisted Quantitative Structure−Activity and Structure−Property Relationship Studies
inferred← citeshttps://doi.org/10.1021/ci049849fopenalex-snapshot
Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors
inferred← citeshttps://doi.org/10.1021/ci0498968openalex-snapshot
Application of the PharmPrint Methodology to Two Protein Kinases
inferred← citeshttps://doi.org/10.1021/ci0499469openalex-snapshot
Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues
inferred← citeshttps://doi.org/10.1021/ci050022aopenalex-snapshot
Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance
inferred← citeshttps://doi.org/10.1021/ci050110vopenalex-snapshot
Interpreting Computational Neural Network Quantitative Structure−Activity Relationship Models: A Detailed Interpretation of the Weights and Biases
inferred← citeshttps://doi.org/10.1021/ci0501342openalex-snapshot
Posetic Quantitative Superstructure/Activity Relationships (QSSARs) for Chlorobenzenes
inferred← citeshttps://doi.org/10.1021/ci050215yopenalex-snapshot
Quantitative Structure−Activity Relationship Modeling of Juvenile Hormone Mimetic Compounds forCulexPipiensLarvae, with a Discussion of Descriptor-Thinning Methods
inferred← citeshttps://doi.org/10.1021/ci060031zopenalex-snapshot
A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines toward α1Adrenoceptors
inferred← citeshttps://doi.org/10.1021/ci600563wopenalex-snapshot
Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models
inferred← citeshttps://doi.org/10.1021/ci800276topenalex-snapshot
Kinase-Targeted Library Design through the Application of the PharmPrint Methodology
inferred← citeshttps://doi.org/10.1021/cr900238dopenalex-snapshot
Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction
inferred← citeshttps://doi.org/10.1021/ct050284jopenalex-snapshot
Semiempirical Comparative Binding Energy Analysis (SE-COMBINE) of a Series of Trypsin Inhibitors
inferred← citeshttps://doi.org/10.1039/d4gc00889hopenalex-snapshot
Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids
inferred← citeshttps://doi.org/10.1080/08927020701564479openalex-snapshot
Comparative modeling of the conformational stability of chymotrypsin inhibitor 2 protein mutants using amino acid sequence autocorrelation (AASA) and amino acid 3D autocorrelation (AA3DA) vectors and ensembles of Bayesian-regularized genetic neural networks
inferred← citeshttps://doi.org/10.1080/10629360902726700openalex-snapshot
Quantitative structure–property relationship modelling of the degradability rate constant of alkenes by OH radicals in atmosphere
inferred← citeshttps://doi.org/10.1080/14756360600862366openalex-snapshot
Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized Genetic Neural Networks and ensemble averaging
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2006.00435.xopenalex-snapshot
Ensembles of Bayesian‐regularized Genetic Neural Networks for Modeling of Acetylcholinesterase Inhibition by Huprines
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2008.00675.xopenalex-snapshot
Proteochemometric Modeling of the Inhibition Complexes of Matrix Metalloproteinases with N‐Hydroxy‐2‐[(Phenylsulfonyl)Amino]Acetamide Derivatives Using Topological Autocorrelation Interaction Matrix and Model Ensemble Averaging
inferred← citeshttps://doi.org/10.1186/1758-2946-1-22openalex-snapshot
Interpretable correlation descriptors for quantitative structure-activity relationships
inferred← citeshttps://doi.org/10.1186/1758-2946-6-8openalex-snapshot
Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
inferred← citeshttps://doi.org/10.1517/17460441.2010.492827openalex-snapshot
Advances in computational methods to predict the biological activity of compounds
inferred← citeshttps://doi.org/10.2174/156802612804910278openalex-snapshot
A Survey of Quantitative Descriptions of Molecular Structure
inferred← citeshttps://doi.org/10.2478/s11696-013-0321-0openalex-snapshot
A new, fully validated and interpreted quantitative structure-activity relationship model of p-aminosalicylic acid derivatives as neuraminidase inhibitors
inferred← citeshttps://doi.org/10.2751/jcac.13.1openalex-snapshot
Powerful Integrative Tool Combining Structure Generator and Chemical Space Visualization
inferred← citeshttps://doi.org/10.3109/14756366.2013.866659openalex-snapshot
Novel phenolic inhibitors of the sarco/endoplasmic reticulum calcium ATPase: identification and characterization by quantitative structure–activity relationship modeling and virtual screening
inferred← citeshttps://doi.org/10.3233/ci-2008-0015openalex-snapshot
Improving Usability and Accessibility of Cheminformatics Tools for Chemists through Cyberinfrastructure and Education
inferred← citeshttps://doi.org/10.37285/ijddd.4.3.4openalex-snapshot
3D-QSAR Studies Combined with Virtual Screening to Identify Novel Inhibitors of N-Acetyl Glucosamine 1-Phosphate Uridyltransferase from Mycobacterium Tuberculosis
inferred← citeshttps://doi.org/10.4018/ijqspr.20200701.oa1openalex-snapshot
Principles of QSAR Modeling
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