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Support Vector Machine and the Heuristic Method to Predict the Solubility of Hydrocarbons in Electrolyte

https://doi.org/10.1021/jp0501446
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What this work says

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

inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/aic.690420629openalex-snapshot
Measuring vapor‐liquid equilibrium for aqueous‐organic systems: Review and a new technique
inferredcites →https://doi.org/10.1002/etc.5620150509openalex-snapshot
PERSISTENCE OF POLYCYCLIC AROMATIC COMPOUNDS OF DIFFERENT MOLECULAR SIZE AND WATER SOLUBILITY IN SURFICIAL SEDIMENT OF AN INTERTIDAL SANDFLAT
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot
Drug design by machine learning: support vector machines for pharmaceutical data analysis
inferredcites →https://doi.org/10.1016/s0378-3812(01)00520-9openalex-snapshot
Thermodynamic analysis of the mutual solubilities of hydrocarbons and water
inferredcites →https://doi.org/10.1016/s0378-3812(99)00021-7openalex-snapshot
Thermodynamic analysis of the mutual solubilities of normal alkanes and water
inferredcites →https://doi.org/10.1021/ci0341161openalex-snapshot
Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
inferredcites →https://doi.org/10.1021/jp011755aopenalex-snapshot
Aqueous Solubility−Molecular Size Relationships: A Mechanistic Case Study Using C10- to C19-Alkanes
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1201/9781482296945openalex-snapshot
Topological Indices and Related Descriptors in QSAR and QSPR

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
inferred← citeshttps://doi.org/10.1002/cem.2490openalex-snapshot
A novel tree kernel partial least squares for modeling the structure–activity relationship
inferred← citeshttps://doi.org/10.1002/ejic.200900966openalex-snapshot
Subvalent Organometallic Compounds of the Alkaline Earth Metals in Low Oxidation States
inferred← citeshttps://doi.org/10.1002/qsar.200610078openalex-snapshot
Quantitative Structure–Activity Relationships Study on the Ah Receptor Binding Affinities of Polybrominated Diphenyl Ethers Using a Support Vector Machine
inferred← citeshttps://doi.org/10.1002/qsar.200810009openalex-snapshot
Quantitative Structure–Property Relationship Studies for Predicting Flash Points of Organic Compounds using Support Vector Machines
inferred← citeshttps://doi.org/10.1007/s10462-010-9180-zopenalex-snapshot
Data mining applications in hydrocarbon exploration
inferred← citeshttps://doi.org/10.1007/s10973-013-3587-0openalex-snapshot
Evaluations of thermal decomposition properties for optically active polymers based on support vector machine
inferred← citeshttps://doi.org/10.1016/j.aca.2012.06.031openalex-snapshot
Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification
inferred← citeshttps://doi.org/10.1016/j.chemolab.2008.03.002openalex-snapshot
Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds
inferred← citeshttps://doi.org/10.1016/j.chemolab.2012.11.008openalex-snapshot
A novel tree kernel support vector machine classifier for modeling the relationship between bioactivity and molecular descriptors
inferred← citeshttps://doi.org/10.1016/j.chroma.2006.01.136openalex-snapshot
Quantitative structure–property relationships for pesticides in biopartitioning micellar chromatography
inferred← citeshttps://doi.org/10.1016/j.comtox.2017.07.005openalex-snapshot
QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine
inferred← citeshttps://doi.org/10.1016/j.ejps.2012.11.012openalex-snapshot
QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity
inferred← citeshttps://doi.org/10.1016/j.molliq.2014.11.025openalex-snapshot
Prediction of gas-to-ionic liquid partition coefficient of organic solutes dissolved in 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate using QSPR approaches
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/10.1063/1.4929575openalex-snapshot
Acetic acid dimers in a nitrogen matrix: Observation of structures containing the higher-energy conformer
inferred← citeshttps://doi.org/10.1063/1.4942027openalex-snapshot
Acetic acid–water complex: The first observation of structures containing the higher-energy acetic acid conformer
inferred← citeshttps://doi.org/10.1080/10826070802129001openalex-snapshot
Review of QSPR Modeling of Mobilities of Peptides in Capillary Zone Electrophoresis
inferred← citeshttps://doi.org/10.1109/tcbb.2011.36openalex-snapshot
Recipe for uncovering predictive genes using support vector machines based on model population analysis
inferred← citeshttps://doi.org/10.1142/s0219633607003118openalex-snapshot
B3LYP-SVM METHOD FOR THE ESTIMATION OF MOLECULAR ENTHALPIES OF FORMATION
inferred← citeshttps://doi.org/10.1155/2013/860357openalex-snapshot
An Accurate and Efficient Method to Predict Y-NO Bond Homolysis Bond Dissociation Energies
inferred← citeshttps://doi.org/10.1208/aapsj080104openalex-snapshot
Recent progress in the computational prediction of aqueous solubility and absorption
inferred← citeshttps://doi.org/10.14419/ijet.v2i2.834openalex-snapshot
Applications of Support Vector Machines in the Exploratory Phase of Petroleum and Natural Gas: a Survey
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