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Neural Network Based Temperature-Dependent Quantitative Structure Property Relations (QSPRs) for Predicting Vapor Pressure of Hydrocarbons

https://doi.org/10.1021/ci000462w
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What this work says

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

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A Method for Determining the Theoretical Carcinogenicity of Polycyclic Aromatic Hydrocarbons
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The use of neural networks for variable selection in QSAR
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Neural Network-Graph Theory Approach to the Prediction of the Physical Properties of Organic Compounds
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Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons
inferredcites →https://doi.org/10.1021/ci960176dopenalex-snapshot
Use of Topostructural, Topochemical, and Geometric Parameters in the Prediction of Vapor Pressure: A Hierarchical QSAR Approach
inferredcites →https://doi.org/10.1021/ci970289copenalex-snapshot
QSPR Prediction of Vapor Pressure from Solely Theoretically-Derived Descriptors
inferredcites →https://doi.org/10.1021/ci9803334openalex-snapshot
Molecular Structure Disassembly Program (MOSDAP): A Chemical Information Model To Automate Structure-Based Physical Property Estimation
inferredcites →https://doi.org/10.1021/ci990131nopenalex-snapshot
QM/NN QSPR Models with Error Estimation: Vapor Pressure and LogP
inferredcites →https://doi.org/10.1021/ci990137copenalex-snapshot
Quantitative Structure−Property Relationships for the Prediction of Vapor Pressures of Organic Compounds from Molecular Structures
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The properties of gases and liquids
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Application of Quantitative Structure−Performance Relationship and Neural Network Models for the Prediction of Physical Properties from Molecular Structure
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Characterization of molecular branching
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Importance of nonbonded attraction in the stereochemistry of the SN2' reaction
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The simulation and interpretation of free turbulence with a cognitive neural system

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures
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ChemInform Abstract: Neural Network Based Temperature‐Dependent Quantitative Structure Property Relations (QSPRs) for Predicting Vapor Pressure of Hydrocarbons.
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Physics‐informed Transfer Learning for Out‐of‐sample Vapor Pressure Predictions
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