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QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Binding of the Bioactive Component Daphnetin to Human Serum Albumin Demonstrated Using Tryptophan Fluorescence Quenching
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Estimation of Dependences Based on Empirical Data
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The Nature of Statistical Learning Theory
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Quantum chemistry program exchange
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Interaction of isofraxidin with human serum albumin
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Three-Dimensional Quantitative Structure Activity Relationship Computational Approaches for Prediction of Human In Vitro Intrinsic Clearance
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The Characterization of Two Specific Drug Binding Sites on Human Serum Albumin
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Further Characterization of Specific Drug Binding Sites on Human Serum Albumin
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QSAR Model for Drug Human Oral Bioavailability
inferredcites →https://doi.org/10.1021/jm010960bopenalex-snapshot
Cheminformatic Models To Predict Binding Affinities to Human Serum Albumin
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A Tutorial on Support Vector Machines for Pattern Recognition
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QSAR modeling of β-lactam binding to human serum proteins
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A tutorial on support vector regression
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Atomic structure and chemistry of human serum albumin
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A double-sided radio jet from the compact Galactic Centre annihilator 1E1740.7–2942
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QSPR: the correlation and quantitative prediction of chemical and physical properties from structure
inferredcites →https://doi.org/10.1111/j.1365-2125.1988.tb03318.xopenalex-snapshot
Pharmaceutical innovation by the seven UK‐owned pharmaceutical companies (1964‐1985).
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Structure of Human Serum Albumin
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In Reply: Deprenyl and the Progression of Parkinson's Disease
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Three-Dimensional Structure of Human Serum Albumin
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Estimation of Dependences Based on Empirical Data.
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Genetic algorithms in search, optimization, and machine learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine.
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Good's buffer ionic liquids as relevant phase‐forming components of self‐buffered aqueous biphasic systems
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QSAR Development for Plasma Protein Binding: Influence of the Ionization State
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inferred← citeshttps://doi.org/10.1016/b0-08-045044-x/00151-6openalex-snapshot
In Silico Prediction of Plasma and Tissue Protein Binding
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inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
Chemometrics tools in QSAR/QSPR studies: A historical perspective
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Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design
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Unraveling the versatility of human serum albumin – A comprehensive review of its biological significance and therapeutic potential
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2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2007.10.024openalex-snapshot
QSPR analysis of air-to-blood distribution of volatile organic compounds
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Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure–activity relationships methods
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In silico modelling of the interaction of flavonoids with human P-glycoprotein nucleotide-binding domain
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Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches
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A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM
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Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks
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Results of molecular docking as descriptors to predict human serum albumin binding affinity
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Prediction of gas-to-ionic liquid partition coefficient of organic solutes dissolved in 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate using QSPR approaches
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Prediction of the maximum absorption wavelength of azobenzene dyes by QSPR tools
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Application of support vector machine (SVM) for prediction toxic activity of different data sets
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Evaluation of drug–human serum albumin binding interactions with support vector machine aided online automated docking
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ANN‐QSAR Model of Drug‐binding to Human Serum Albumin
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Factor Analysis of the Relationship of the Chemical Structure and Anti-Radical Activity of Natural Phenols
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Binding study of different drugs with serum albumins
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Prediction of Carcinogenicity of Noncongeneric Chemical Substances by a Support Vector Machine
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