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Chemometric selection of a small set of pharmaceutical active substances used in determining the orthogonality and similarity of chromatographic systems
Chromatographic classification and comparison of commercially available reversed-phase liquid chromatographic columns containing polar embedded groups/amino endcappings using principal component analysis
Chromatographic classification and comparison of commercially available reversed-phase liquid chromatographic columns containing phenyl moieties using principal component analysis
Solute descriptors for characterizing retention properties of open-tubular columns of different selectivity in gas chromatography at intermediate temperatures
Estimation and prediction on retention times of components from essential oil of Paulownia tomentosa flowers by molecular electronegativity-distance vector (MEDV)
Characterisation of reversed-phase liquid chromatographic columns by chromatographic tests. Evaluation of 36 test parameters: repeatability, reproducibility and correlation
Chromatographic classification and comparison of commercially available reversed-phase liquid chromatographic columns using principal component analysis
Molecular mechanism of retention in reversed-phase high-performance liquid chromatography and classification of modern stationary phases by using quantitative structure–retention relationships
Comparison of Ridge Regression, Partial Least-Squares, Pairwise Correlation, Forward- and Best Subset Selection Methods for Prediction of Retention Indices for Aliphatic Alcohols
How Far Would You Commute? Analysis of Long-Distance Work Trips between the Capital City and the Different Levels of Settlement Hierarchy – a Case Study from Hungary
Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid‐infrared spectroscopy
Radial basis function neural networks based on projection pursuit approach and solvatochromic descriptors: single and full column prediction of gas chromatography retention behavior of polychlorinated biphenyls
Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites
Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine
Consensus‐based comparison of chromatographic and computationally estimated lipophilicity of benzothiepino[3,2‐ c ]pyridine derivatives as potential antifungal drugs
Generic reversed‐phase ultra‐high‐pressure liquid chromatography methodology developed by using computer‐assisted modeling for streamlined performance evaluation of a wide range of stationary phase columns
Statistical analysis-based green planar chromatographic methodology for the quality assessment of food supplements: a case study on Origanum vulgare L. commercial products
Development of the HPLC Method for Simultaneous Determination of Lidocaine Hydrochloride and Tribenoside Along with Their Impurities Supported by the QSRR Approach
A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models
CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information
CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products
How to rank and discriminate artificial neural networks? Case study: prediction of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives
Ranking and similarity for quantitative structure–retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons
Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis
Molecular docking and QSAR analysis on maleimide derivatives selective inhibition against human monoglyceride lipase based on various modeling methods and conformations
Improved classification of fused data: Synergetic effect of partial least squares discriminant analysis (PLS-DA) and common components and specific weights analysis (CCSWA) combination as applied to tomato profiles (NMR, IR and IRMS)
First report on development of quantitative interspecies structure–carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines
Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids
Comparison of core–shell and totally porous ultra high performance liquid chromatographic stationary phases based on their selectivity towards alfuzosin compounds
Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography
Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, log P , and retention factor ratio to identify optimal analyte training sets for ion chromatography
Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model
Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks
Chemometrics of anisotropic lipophilicity of anticancer androstane derivatives determined by reversed-phase ultra high performance liquid chromatography with polar aprotic and protic modifiers
On the characterization of novel biologically active steroids: Selection of lipophilicity models of newly synthesized steroidal derivatives by classical and non-parametric ranking approaches
Multi-objective optimization with post-pareto optimality analysis for the integration of storage systems with reactive-power compensation in distribution networks
Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies
Reliable prediction of the condensed (solid or liquid) phase enthalpy of formation of organic energetic materials at 298 K through their molecular structures
Optimization of particle number, substrate concentration and temperature of batch immobilized reactor system for biohydrogen production by dark fermentation
A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression
Toward steroidal anticancer drugs: Non-parametric and 3D-QSAR modeling of 17-picolyl and 17-picolinylidene androstanes with antiproliferative activity on breast adenocarcinoma cells
Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings
Comprehensive evaluation of radical scavenging, reducing power and chelating capacity of free proteinogenic amino acids using spectroscopic assays and multivariate exploratory techniques
Non-parametric partial least squares–discriminant analysis model based on sum of ranking difference algorithm for tea grade identification using electronic tongue data
Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra
Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches
Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following Organisation for Economic Co-operation and Development Test Guidelines 407, 408, and 422 Supported by Experimental Validation
Connected Subgraph Fingerprints: Representing Molecules Using Exhaustive Subgraph Enumeration
showing the first 200 of 300
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