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Development of Quantitative Structure−Activity Relationship and Classification Models for a Set of Carbonic Anhydrase Inhibitors

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What this work says

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

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Atomic charge calculations for quantitative structure—property relationships
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Suitability of the PM3‐derived molecular electrostatic potentials
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Computer assisted studies of chemical structure and biological function
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Physical Factors Contributing to Hydrophobic Constant π
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MOPAC: A semiempirical molecular orbital program
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Molecular connectivity in structure-activity analysis
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Quantum chemistry program exchange
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Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks
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Molecular shape and the prediction of high-performance liquid chromatographic retention indexes of polycyclic aromatic hydrocarbons
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Electron impact excitation of ions in Fourier transform mass spectrometry
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Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies
inferredcites →https://doi.org/10.1021/ci00023a011openalex-snapshot
Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing
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On molecular identification numbers
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A simple method for the representation, quantification, and comparison of the volumes and shapes of chemical compounds
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Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure
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Prediction of Inhibition of the Sodium Ion−Proton Antiporter by Benzoylguanidine Derivatives from Molecular Structure
inferredcites →https://doi.org/10.1021/ci990125ropenalex-snapshot
Prediction of IC50Values for ACAT Inhibitors from Molecular Structure
inferredcites →https://doi.org/10.1021/ja01193a005openalex-snapshot
Structural Determination of Paraffin Boiling Points
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A TEXT-BOOK OF ORGANIC CHEMISTRY.
inferredcites →https://doi.org/10.1021/jm000244uopenalex-snapshot
Classification of Multidrug-Resistance Reversal Agents Using Structure-Based Descriptors and Linear Discriminant Analysis
inferredcites →https://doi.org/10.1021/jm00061a023openalex-snapshot
Statistics using neural networks: chance effects
inferredcites →https://doi.org/10.1021/jm00196a017openalex-snapshot
Chance factors in studies of quantitative structure-activity relationships
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A preliminary structure-activity study of the mixed-function oxidase inhibitor, 7,8-benzoflavone
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A 3D QSAR Study of a Series of HEPT Analogues: The Influence of Conformational Mobility on HIV-1 Reverse Transcriptase Inhibition
inferredcites →https://doi.org/10.1097/00061198-199502000-00012openalex-snapshot
The Development of Topical Carbonic Anhydrase Inhibitors
inferredcites →https://doi.org/10.5860/choice.33-5718openalex-snapshot
Computer software applications in chemistry

What points at this work

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

inferred← citeshttps://doi.org/10.1002/0471266949.bmc002openalex-snapshot
Recent Trends in Quantitative Structure‐Activity Relationships
inferred← citeshttps://doi.org/10.1002/9780470508169.ch18openalex-snapshot
QSAR of Carbonic Anhydrase Inhibitors and Their Impact on Drug Design
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Bibliography
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A Perspective on Using Immersive Analytics With Virtual Reality for One‐Class Classification Decisions
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ChemInform Abstract: Development of Quantitative Structure—Activity Relationship and Classification Models for a Set of Carbonic Anhydrase Inhibitors.
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Genetic Neural Networks for Functional Approximation
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Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis
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Non-linear quantitative structure–activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase
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A physically interpretable quantum-theoretic QSAR for some carbonic anhydrase inhibitors with diverse aromatic rings, obtained by a new QSAR procedure
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Applying data mining techniques to library design, lead generation and lead optimization
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QSARs and activity predicting models for competitive inhibitors of adenosine deaminase
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Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors
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Computational modeling of in vitro biological responses on polymethacrylate surfaces
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Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates
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Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity
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QSAR and Classification of Murine and Human Soluble Epoxide Hydrolase Inhibition by Urea-Like Compounds
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Development of Binary Classification of Structural Chromosome Aberrations for a Diverse Set of Organic Compounds from Molecular Structure
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Predicting the Genotoxicity of Polycyclic Aromatic Compounds from Molecular Structure with Different Classifiers
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Probabilistic Neural Network Multiple Classifier System for Predicting the Genotoxicity of Quinolone and Quinoline Derivatives
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Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods
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Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem
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Detour matrix-based adjacent path eccentric distance sum indices for QSAR/QSPR. Part I: development and evaluation
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A perspective on quantitative structure–activity relationships and carbonic anhydrase inhibitors
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Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model
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