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Quantitative Structure−Activity Relationship Studies of Progesterone Receptor Binding Steroids

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What this work says

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

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Evolutionary variable selection in regression and PLS analyses

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GA Strategy for Variable Selection in QSAR Studies: Enhancement of Comparative Molecular Binding Energy Analysis by GA-Based PLS Method

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Chemometric Methods in Molecular Design

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Genetic algorithms as a strategy for feature selection

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Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection

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Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution

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Comparison of Forward Selection, Backward Elimination, and Generalized Simulated Annealing for Variable Selection

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A comparison of progestin and androgen receptor binding using the CoMFA technique

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Binding of progestagens to receptor proteins in MCF-7 cells

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Automatic generation of 3D-atomic coordinates for organic molecules

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Pattern recognition for the study of structure—activity relationships

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GA strategy for variable selection in QSAR studies: GAPLS and D-optimal designs for predictive QSAR model

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Random correlation in variable selection for multivariate calibration with a genetic algorithm

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Genetic algorithms applied to feature selection in PLS regression: how and when to use them

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Computational neural networks as model-free mapping devices

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Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing

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Prediction of Aqueous Solubility for a Diverse Set of Heteroatom-Containing Organic Compounds Using a Quantitative Structure−Property Relationship

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GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists

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Prediction of Supercritical Carbon Dioxide Solubility of Organic Compounds from Molecular Structure

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Prediction of Autoignition Temperatures of Organic Compounds from Molecular Structure

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GA Strategy for Variable Selection in QSAR Studies: GA-Based Region Selection for CoMFA Modeling

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Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure

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Influence of 6-azido and 6-thiocyanato substitution on progestational and corticoid activities and a structure-activity correlation in the .DELTA.6-6-substituted progestational series

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N-Alkylnorketobemidones with strong agonist and weak antagonist properties

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Binding of steroids to the progestin and glucocorticoid receptors analyzed by correspondence analysis

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Tricyclic compounds as selective antimuscarinics. 2. Structure-activity relationships of M1-selective antimuscarinics related to pirenzepine

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Equation of State Calculations by Fast Computing Machines

inferredcites →https://doi.org/10.1080/01621459.1993.10476299openalex-snapshot

Linear Model Selection by Cross-Validation

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Structure of 19-nor-17α-pregna-4,15-dien-20-yn-17β-ol

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Optimization by Simulated Annealing

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What points at this work

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

inferred← citeshttps://doi.org/10.1002/9783527628766.biblioopenalex-snapshot

Bibliography

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ChemInform Abstract: Quantitative Structure—Activity Relationship Studies of Progesterone Receptor Binding Steroids.

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Rational Design, Synthesis, and Biological Evaluation of Progesterone-Modified MRI Contrast Agents

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Chemometrics tools in QSAR/QSPR studies: A historical perspective

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Cross-validation as the objective function for variable-selection techniques

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Artificial neural networks and genetic algorithms in QSAR

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Fast Computation of Cross-Validated Properties in Full Linear Leave-Many-Out Procedures

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QSAR Modeling of Genotoxicity on Non-congeneric Sets of Organic Compounds

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StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

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Molecular docking and 3D-QSAR of 16α,17α-cycloalkanoprogesterone derivatives as ligands of the progesterone receptor

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Molecular modeling of mono- and bis-quaternary ammonium salts as ligands at the α4β2 nicotinic acetylcholine receptor subtype using nonlinear techniques

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Data Mining of Structure-Activity Data Through Genetic Algorithm and Counter Propagation Neural Network

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Feature Selection Methods in QSAR Studies

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