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Evolutionary Optimization in Quantitative Structure−Activity Relationship: An Application of Genetic Neural Networks

https://doi.org/10.1021/jm9507035
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Chemometric Methods in Molecular Design
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Chiral chromatography and multivariate quantitative structure-property relationships of benzimidazole sulphoxides
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QSAR: Hansch analysis and related approaches
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Structure-Activity Study on Anticonvulsant (Thio) Hydantoins Using Molecular Connectivity Indices
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Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing
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Manganese(I)-induced 1,6-demethanation across the carbon-carbon triple bond of linear alkynes in the gas phase. A case for the generation of manganese cycloalkynes?
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Molecular Similarity Matrixes and Quantitative Structure-Activity Relationships: A Case Study with Methodological Implications
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Synthesis and DNA-sequence selectivity of a series of mono- and difunctional 9-aminoacridine nitrogen mustards
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Molecular Similarity, Quantitative Chirality, and QSAR for Chiral Drugs
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Analysis of linear and nonlinear QSAR data using neural networks
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A Unified Framework for Using Neural Networks To Build QSARs
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Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
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Structure-activity relationships of antifilarial antimycin analogs: a multivariate pattern recognition study
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Introduction to the Theory of Neural Computation
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Applied Regression Analysis and Experimental Design.
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Applications of artificial intelligence in chemistry

What points at this work

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Consensus scoring for protein–ligand interactions
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Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses
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Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research
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Effect of Chromium on CCT Diagrams of Novel Air-Cooled Bainite Steels Analyzed by Neural Network
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RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels
inferred← citeshttps://doi.org/10.1016/s1093-3263(01)00122-xopenalex-snapshot
Enhancement of binary QSAR analysis by a GA-based variable selection method
inferred← citeshttps://doi.org/10.1016/s1359-6446(05)03632-9openalex-snapshot
Keynote review: In vitro safety pharmacology profiling: an essential tool for successful drug development
inferred← citeshttps://doi.org/10.1016/s1359-6446(96)10045-3openalex-snapshot
Evolutionary and genetic methods in drug design
inferred← citeshttps://doi.org/10.1016/s1359-6446(98)01219-7openalex-snapshot
Evolutionary combinatorial chemistry: application of genetic algorithms
inferred← citeshttps://doi.org/10.1016/s1367-5931(98)80013-6openalex-snapshot
Applications of genetic algorithms in molecular diversity
inferred← citeshttps://doi.org/10.1016/s1380-7323(07)80016-1openalex-snapshot
Chapter 15 A quantitative structure-activity relationship of 1,4-dihydropyridine calcium channel blockers with electronic descriptors produced by quantum chemical topology
inferred← citeshttps://doi.org/10.1016/s1570-7946(04)80110-xopenalex-snapshot
Development of nonlinear quantitative structure-activity relationships using rbf networks and evolutionary computing
inferred← citeshttps://doi.org/10.1016/s1574-1400(06)02007-xopenalex-snapshot
Chapter 7 Variable Selection QSAR Modeling, Model Validation, and Virtual Screening
inferred← citeshttps://doi.org/10.1016/s1734-1140(12)70873-3openalex-snapshot
Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00934openalex-snapshot
Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies
inferred← citeshttps://doi.org/10.1021/acs.jctc.8b00895openalex-snapshot
Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network
inferred← citeshttps://doi.org/10.1021/acs.jpcb.0c01370openalex-snapshot
Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy
inferred← citeshttps://doi.org/10.1021/acs.jpclett.8b01131openalex-snapshot
Force Field for Water Based on Neural Network
inferred← citeshttps://doi.org/10.1021/acs.macromol.6b01747openalex-snapshot
Designing Sequence-Specific Copolymer Compatibilizers Using a Molecular-Dynamics-Simulation-Based Genetic Algorithm
inferred← citeshttps://doi.org/10.1021/acschemneuro.8b00227openalex-snapshot
Identification of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Acting at Site Distinct from 2-Methyl-6-(phenylethynyl)-pyridine Binding
inferred← citeshttps://doi.org/10.1021/ci0203702openalex-snapshot
On the Use of Neural Network Ensembles in QSAR and QSPR
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/ci100103ropenalex-snapshot
Replacement Method and Enhanced Replacement Method Versus the Genetic Algorithm Approach for the Selection of Molecular Descriptors in QSPR/QSAR Theories
inferred← citeshttps://doi.org/10.1021/ci200079bopenalex-snapshot
Advances in the Replacement and Enhanced Replacement Method in QSAR and QSPR Theories
inferred← citeshttps://doi.org/10.1021/ci900283jopenalex-snapshot
A Novel Structure-Based Multimode QSAR Method Affords Predictive Models for Phosphodiesterase Inhibitors
inferred← citeshttps://doi.org/10.1021/ci980008gopenalex-snapshot
Evaluation of Quantitative Structure−Activity Relationship Methods for Large-Scale Prediction of Chemicals Binding to the Estrogen Receptor
inferred← citeshttps://doi.org/10.1021/ci980033mopenalex-snapshot
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
inferred← citeshttps://doi.org/10.1021/ci980161aopenalex-snapshot
A New Efficient Approach for Variable Selection Based on Multiregression: Prediction of Gas Chromatographic Retention Times and Response Factors
inferred← citeshttps://doi.org/10.1021/ct5001044openalex-snapshot
General Multiobjective Force Field Optimization Framework, with Application to Reactive Force Fields for Silicon Carbide
inferred← citeshttps://doi.org/10.1021/jm0104668openalex-snapshot
Feature Selection for Structure−Activity Correlation Using Binary Particle Swarms
inferred← citeshttps://doi.org/10.1021/jm201371yopenalex-snapshot
Cellular Quantitative Structure–Activity Relationship (Cell-QSAR): Conceptual Dissection of Receptor Binding and Intracellular Disposition in Antifilarial Activities of Selwood Antimycins
inferred← citeshttps://doi.org/10.1021/jp045546copenalex-snapshot
Automatic Method for Identifying Reaction Coordinates in Complex Systems
inferred← citeshttps://doi.org/10.1021/jp101476gopenalex-snapshot
Extracting Physically Intuitive Reaction Coordinates from Transition Networks of a β-Sheet Miniprotein
inferred← citeshttps://doi.org/10.1021/jp3020386openalex-snapshot
Modified Feed-Forward Neural Network Structures and Combined-Function-Derivative Approximations Incorporating Exchange Symmetry for Potential Energy Surface Fitting
inferred← citeshttps://doi.org/10.1021/jp401742yopenalex-snapshot
New Insights into the Folding of a β-Sheet Miniprotein in a Reduced Space of Collective Hydrogen Bond Variables: Application to a Hydrodynamic Analysis of the Folding Flow
inferred← citeshttps://doi.org/10.1023/a:1008073215919openalex-snapshot
A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors
inferred← citeshttps://doi.org/10.1023/a:1008368105614openalex-snapshot
Application of Cascade Correlation Networks for Structures to Chemistry
inferred← citeshttps://doi.org/10.1023/a:1010913017092openalex-snapshot
Role of Genetic Algorithms and Artificial Neural Networks in Predicting the Phase Behavior of Colloidal Delivery Systems
inferred← citeshttps://doi.org/10.1023/a:1011945119287openalex-snapshot
Evaluation of designed ligands by a multiple screening method: Application to glycogen phosphorylase inhibitors constructed with a variety of approaches
inferred← citeshttps://doi.org/10.1023/a:1020643331894openalex-snapshot
Protein Structure Prediction in a 210-Type Lattice Model: Parameter Optimization in the Genetic Algorithm Using Orthogonal Array
inferred← citeshttps://doi.org/10.1023/a:1021928016359openalex-snapshot
Genetic algorithm for the design of molecules with desired properties
inferred← citeshttps://doi.org/10.1037//1040-3590.12.1.40openalex-snapshot
Applying artificial neural network models to clinical decision making.
inferred← citeshttps://doi.org/10.1038/aps.2016.4openalex-snapshot
Phenylphthalazines as small-molecule inhibitors of urea transporter UT-B and their binding model
inferred← citeshttps://doi.org/10.1038/srep05775openalex-snapshot
Developing Hypothetical Inhibition Mechanism of Novel Urea Transporter B Inhibitor
inferred← citeshttps://doi.org/10.1038/srep19312openalex-snapshot
Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products
inferred← citeshttps://doi.org/10.1039/c0an00387eopenalex-snapshot
Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data
inferred← citeshttps://doi.org/10.1039/c1cp21668fopenalex-snapshot
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
inferred← citeshttps://doi.org/10.1039/c2an15972dopenalex-snapshot
Interpolation and extrapolation problems of multivariate regression in analytical chemistry: benchmarking the robustness on near-infrared (NIR) spectroscopy data
inferred← citeshttps://doi.org/10.1039/c5ra10729fopenalex-snapshot
Multivariate statistical analysis methods in QSAR
inferred← citeshttps://doi.org/10.1063/1.3206326openalex-snapshot
Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies
inferred← citeshttps://doi.org/10.1063/1.4934782openalex-snapshot
Reducing the cost of evaluating the committor by a fitting procedure
inferred← citeshttps://doi.org/10.1063/1.5018708openalex-snapshot
Learning free energy landscapes using artificial neural networks
inferred← citeshttps://doi.org/10.1063/5.0248589openalex-snapshot
Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions
inferred← citeshttps://doi.org/10.1080/00032719908542831openalex-snapshot
Genetic Algorithms in Analytical Chemistry
inferred← citeshttps://doi.org/10.1080/00268976.2024.2331620openalex-snapshot
Applications of conceptual density functional theory in reference to quantitative structure–activity / property relationship
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/10.1080/08927022.2014.907898openalex-snapshot
Recent developments in methods for identifying reaction coordinates
inferred← citeshttps://doi.org/10.1080/10629360008033232openalex-snapshot
A General13C NMR Spectrum Predictor Using Data Mining Techniques
inferred← citeshttps://doi.org/10.1080/1062936021000058773openalex-snapshot
Fuzzy structure-activity relationships
inferred← citeshttps://doi.org/10.1080/10629360290014296openalex-snapshot
Variable selection for QSAR by artificial ant colony systems
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1018941openalex-snapshot
The application of machine learning to the modelling of percutaneous absorption: An overview and guide
inferred← citeshttps://doi.org/10.1080/1062936x.2018.1424030openalex-snapshot
QSAR modelling using combined simple competitive learning networks and RBF neural networks
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/10.1080/17460441.2018.1547278openalex-snapshot
An overview of neural networks for drug discovery and the inputs used
inferred← citeshttps://doi.org/10.1080/18756891.2012.696909openalex-snapshot
Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction
inferred← citeshttps://doi.org/10.1080/24701556.2026.2668648openalex-snapshot
Synergistic integration of inorganic nanoparticles and biosurfactants: Enhancing stability and efficacy for sustainable remediation and diabetes therapy
inferred← citeshttps://doi.org/10.1088/0953-8984/26/18/183001openalex-snapshot
Representing potential energy surfaces by high-dimensional neural network potentials
inferred← citeshttps://doi.org/10.1093/bib/bbab158openalex-snapshot
Machine learning builds full-QM precision protein force fields in seconds
inferred← citeshttps://doi.org/10.1093/bib/bbn021openalex-snapshot
Computational intelligence approaches for pattern discovery in biological systems
inferred← citeshttps://doi.org/10.1093/bioinformatics/btr628openalex-snapshot
Discovering transcription factor regulatory targets using gene expression and binding data
inferred← citeshttps://doi.org/10.1093/protein/12.11.909openalex-snapshot
Use of a quantitative structure–property relationship to design larger model proteins that fold rapidly
inferred← citeshttps://doi.org/10.1093/protein/gzg077openalex-snapshot
Optimizing the search algorithm for protein engineering by directed evolution
inferred← citeshttps://doi.org/10.1108/17506120810903971openalex-snapshot
Modeling the effects of physician‐directed promotion using genetic algorithm‐partial least squares
inferred← citeshttps://doi.org/10.1109/aici.2009.440openalex-snapshot
Peptide QSARs Study by a Novel Structure Representation Strategy
inferred← citeshttps://doi.org/10.1109/ccdc.2013.6561862openalex-snapshot
The design of digit recognition teaching experiment based on BP neural network
inferred← citeshttps://doi.org/10.1109/cec.2002.1006233openalex-snapshot
Evolved neural networks for quantitative structure-activity relationships of anti-HIV compounds
inferred← citeshttps://doi.org/10.1109/cec.2005.1554829openalex-snapshot
Particle Swarms for Drug Design
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