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Toward an Optimal Procedure for Variable Selection and QSAR Model Building
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Atomistic Topological Indices Applied to Benzodiazepines using Various Regression Methods
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Holographic QSAR of Benzodiazepines
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A Comparative Molecular Field Analysis of Inhibitors of Tubulin Polymerization
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Chemometric Methods in Molecular Design
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Study of Benzodiazepines Receptor Sites Using a Combined QSAR‐CoMFA Approach
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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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Neural Network Studies. 2. Variable Selection
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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 Human Intestinal Absorption of Drug Compounds from Molecular Structure
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Multivariate Regression Outperforms Several Robust Architectures of Neural Networks in QSAR Modeling
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Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture
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Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure−Activity Relationship Studies
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Prediction of Hydroxyl Radical Rate Constants from Molecular Structure
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Genetic Neural Networks for Quantitative Structure−Activity Relationships: Improvements and Application of Benzodiazepine Affinity for Benzodiazepine/GABAA Receptors
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Designing Libraries with CNS Activity
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Letters to the Editor
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Basic Mechanisms of Monogenic Inheritance
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Learning Internal Representations by Error Propagation
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QSAR Analysis of Polyamine Transport Inhibitors in L1210 Cells
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Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study
inferred← citeshttps://doi.org/10.1021/jf900375xopenalex-snapshot
Global Antifungal Profile Optimization of Chlorophenyl Derivatives against Botrytis cinerea and Colletotrichum gloeosporioides
inferred← citeshttps://doi.org/10.1021/jm0104668openalex-snapshot
Feature Selection for Structure−Activity Correlation Using Binary Particle Swarms
inferred← citeshttps://doi.org/10.1021/jp052223nopenalex-snapshot
Accurate Quantitative Structure−Property Relationship Model To Predict the Solubility of C 60 in Various Solvents Based on a Novel Approach Using a Least-Squares Support Vector Machine
inferred← citeshttps://doi.org/10.1021/tx0601509openalex-snapshot
QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles
inferred← citeshttps://doi.org/10.1023/a:1020869118689openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
inferred← citeshttps://doi.org/10.1023/a:1021372108686openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
inferred← citeshttps://doi.org/10.1023/a:1025338411016openalex-snapshot
Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression
inferred← citeshttps://doi.org/10.1023/b:modi.0000047502.66802.3dopenalex-snapshot
Counter-propagation artificial neural network as a tool for the independent variable selection: Structure-mutagenicity study on aromatic amines
inferred← citeshttps://doi.org/10.1039/9781849737203-00172openalex-snapshot
High throughput technology: approaches of research in homogeneous and heterogeneous catalysis
inferred← citeshttps://doi.org/10.1039/d0nj05298aopenalex-snapshot
Structural characterization and QSAR modeling of 1,2,4-triazole derivatives as α-glucosidase inhibitors
inferred← citeshttps://doi.org/10.1039/d6ta00771fopenalex-snapshot
From machine learning to chemical insight: Darwinian chance and the stability of charge carriers in flow batteries
inferred← citeshttps://doi.org/10.1063/1.4743954openalex-snapshot
Exploiting time-independent Hamiltonian structure as controls for manipulating quantum dynamics
inferred← citeshttps://doi.org/10.1080/07391102.2022.2154269openalex-snapshot
Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors
inferred← citeshttps://doi.org/10.1080/08927020500134144openalex-snapshot
2D Autocorrelation modeling of the activity of trihalobenzocycloheptapyridine analogues as farnesyl protein transferase inhibitors
inferred← citeshttps://doi.org/10.1080/08927020500474300openalex-snapshot
Predicting thermochemical parameters of oxygen-containing heterocycles using simple QSPR models
inferred← citeshttps://doi.org/10.1080/10629360902724127openalex-snapshot
A modified uncorrelated linear discriminant analysis model coupled with recursive feature elimination for the prediction of bioactivity
inferred← citeshttps://doi.org/10.1080/10629360903563250openalex-snapshot
Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals
inferred← citeshttps://doi.org/10.1080/10629361003773955openalex-snapshot
Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods
inferred← citeshttps://doi.org/10.1080/1062936x.2012.762425openalex-snapshot
Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1095238openalex-snapshot
QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA
inferred← citeshttps://doi.org/10.1080/1062936x.2016.1238010openalex-snapshot
Classification of biodegradable materials using QSAR modelling with uncertainty estimation
inferred← citeshttps://doi.org/10.1080/1062936x.2024.2320143openalex-snapshot
First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across
inferred← citeshttps://doi.org/10.1080/17460441.2018.1542428openalex-snapshot
Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR)
inferred← citeshttps://doi.org/10.1080/22297928.2016.1209430openalex-snapshot
Quantitative Structure-Property Relationship Study to Predict the Retention Times of Some Volatile Compounds in Rosé Wines
inferred← citeshttps://doi.org/10.1093/bioinformatics/btz521openalex-snapshot
MaNGA: a novel multi-niche multi-objective genetic algorithm for QSAR modelling
inferred← citeshttps://doi.org/10.1109/cec.2005.1554829openalex-snapshot
Particle Swarms for Drug Design
inferred← citeshttps://doi.org/10.1109/csbw.2005.5openalex-snapshot
A Comparison of Particle Swarms Techniques for the Development of Quantitative Structure-Activity Relationship Models for Drug Design
inferred← citeshttps://doi.org/10.1111/cbdd.12815openalex-snapshot
Predicting subtype selectivity of dopamine receptor ligands with three‐dimensional biologically relevant spectrum
inferred← citeshttps://doi.org/10.1111/cbdd.14164openalex-snapshot
A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques
inferred← citeshttps://doi.org/10.1111/j.1744-697x.2008.00116.xopenalex-snapshot
Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2006.00379.xopenalex-snapshot
A QSAR Model of hERG Binding Using a Large, Diverse, and Internally Consistent Training Set
inferred← citeshttps://doi.org/10.1155/2014/279618openalex-snapshot
Identification of Levothyroxine Antichagasic Activity through Computer-Aided Drug Repurposing
inferred← citeshttps://doi.org/10.1155/2017/4914272openalex-snapshot
A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters
inferred← citeshttps://doi.org/10.1186/1758-2946-5-9openalex-snapshot
Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons
inferred← citeshttps://doi.org/10.1186/s12859-016-1379-9openalex-snapshot
Novel group-based QSAR and combinatorial design of CK-1δ inhibitors as neuroprotective agents
inferred← citeshttps://doi.org/10.1186/s12936-019-2941-5openalex-snapshot
Quantitative structure–activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks
inferred← citeshttps://doi.org/10.1186/s13321-017-0256-5openalex-snapshot
An automated framework for QSAR model building
inferred← citeshttps://doi.org/10.1186/s13321-024-00894-1openalex-snapshot
Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow
inferred← citeshttps://doi.org/10.1248/cpb.c17-00729openalex-snapshot
Quality Characterization of Japanese Medicinal Paeoniae Radix by Metallomic Analysis
inferred← citeshttps://doi.org/10.1360/zb2008-38-6-461openalex-snapshot
面向毒害有机物生态风险评价的(Q)SAR技术: 进展与展望
inferred← citeshttps://doi.org/10.1385/1-59259-802-1:131openalex-snapshot
Methods for Applying the Quantitative Structure-Activity Relationship Paradigm
inferred← citeshttps://doi.org/10.1504/ijbra.2016.077123openalex-snapshot
QSAR study of 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using genetic algorithms and artificial neural networks
inferred← citeshttps://doi.org/10.1517/17460441.2.12.1567openalex-snapshot
On some aspects of validation of predictive quantitative structure–activity relationship models
inferred← citeshttps://doi.org/10.1517/17460441.2011.586689openalex-snapshot
Using artificial neural networks to predict cell-penetrating compounds
inferred← citeshttps://doi.org/10.1556/achrom.22.2010.3.2openalex-snapshot
Quantitative structure-retention relationship (QSRR) models for predicting the GC retention times of essential oil components
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1465758/v1openalex-snapshot
HQSAR, CoMFA, CoMSIA docking studies and simulation MD on quinazolines/quinolones derivatives for DENV virus inhibitory activity
inferred← citeshttps://doi.org/10.2174/978160805379711201010194openalex-snapshot
Overview of QSAR Modelling in Rational Drug Design
inferred← citeshttps://doi.org/10.2174/9789815179934123010004openalex-snapshot
Quantitative Structure-activity Relationship (QSAR) in Studying the Biologically Active Molecules
inferred← citeshttps://doi.org/10.3233/ida-194477openalex-snapshot
Balanced training/test set sampling for proper evaluation of classification models
inferred← citeshttps://doi.org/10.3389/fddsv.2022.1082065openalex-snapshot
Drug repurposing screening validated by experimental assays identifies two clinical drugs targeting SARS-CoV-2 main protease
inferred← citeshttps://doi.org/10.3389/fphar.2023.1193282openalex-snapshot
Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro
inferred← citeshttps://doi.org/10.3390/ijms10052107openalex-snapshot
Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines
inferred← citeshttps://doi.org/10.3390/molecules23123271openalex-snapshot
Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition
inferred← citeshttps://doi.org/10.3390/molecules24091698openalex-snapshot
Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling
inferred← citeshttps://doi.org/10.3390/molecules26071996openalex-snapshot
Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches
inferred← citeshttps://doi.org/10.35537/10915/43315openalex-snapshot
Búsqueda racional de nuevos fármacos antichagásicos inhibidores de la cruzipaína
inferred← citeshttps://doi.org/10.35537/10915/51575openalex-snapshot
Topología molecular aplicada a la búsqueda de nuevos fármacos para el tratamiento de la epilepsia refractaria
inferred← citeshttps://doi.org/10.3934/mbe.2014.11.331openalex-snapshot
Designing neural networks for modeling biological data: A statistical perspective
inferred← citeshttps://doi.org/10.4155/fmc-2018-0314openalex-snapshot
Deep Learning and Virtual Drug Screening
inferred← citeshttps://doi.org/10.5012/jkcs.2014.58.6.543openalex-snapshot
Quantitative Structure Activity Relationship Prediction of Oral Bioavailabilities Using Support Vector Machine
inferred← citeshttps://doi.org/10.5740/jaoacint.sge_goodarziopenalex-snapshot
Feature Selection Methods in QSAR Studies
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