QSPR Models for the Physicochemical Properties of Polychlorinated Naphthalene Congeners

2013 ◽  
Vol 726-731 ◽  
pp. 440-443
Author(s):  
Jian Qing Zhu ◽  
Wei Wang ◽  
Hui Ying Xu
Keyword(s):  
Physicochemical Properties ◽  
Water Solubility ◽  
Molecular Volume ◽  
Structure Property ◽  
Polychlorinated Naphthalenes ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Pressure Water ◽  
Quantum Chemical Parameter ◽  
Polychlorinated Naphthalene

Based on quantitative structureproperty relationship (QSPR) of organic compounds, geometrical optimization and quantum chemical parameter calculations have been performed at the B3LYP/6-31G* level of theory for 75 polychlorinated naphthalenes (PCNs). A number of statistically-based parameters have been obtained. Relationship between the physicochemical properties of polychlorinated naphthalene compounds (n-octanol/air partition coefficient, sub-cooled liquid vapor pressure, water solubility) and the structural descriptors have been established by multiple linear regression (MLR) method. The results show that the molecular volume (Vmc), dipolar moment (μ), and the energy of lowest unoccupied molecular orbital (ELUMO), together with the quantity derived from electrostatic potential () can be well used to express the quantitative structure-property relationships of polychlorinated naphthalene compounds. The models constructed have good robustness and high predictive capability.

scholarly journals Prediction of Parallel Artificial Membrane Permeability Assay of Some Drugs from their Theoretically Calculated Molecular Descriptors

2011 ◽  
Vol 8 (3) ◽  
pp. 1074-1085
Author(s):  
E. Konoz ◽  
Amir H. M. Sarrafi ◽  
S. Ardalani
Keyword(s):  
Neural Network ◽  
Membrane Permeability ◽  
Selection Procedure ◽  
Structure Property ◽  
Artificial Membrane ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Artificial Neural ◽  
Variable Subset Selection ◽  
Mlr Model

Parallel artificial membrane permeation assays (PAMPA) have been extensively utilized to determine the drug permeation potentials. In the present work, the permeation of miscellaneous drugs measured as flux by PAMPA (logF) of 94 drugs, are predicted by quantitative structure property relationships modeling based on a variety of calculated theoretical descriptors, which screened and selected by genetic algorithm (GA) variable subset selection procedure. These descriptors were used as inputs for generated artificial neural networks. After generation, optimization and training of artificial neural network (5:3:1), it was used for the prediction of logF for the training, test and validation sets. The standard error for the GA-ANN calculated logF for training, test and validation sets are 0.17, 0.028 and 0.15 respectively, which are smaller than those obtained by GA-MLR model (0.26, 0.051 and 0.22, respectively). Results obtained reveal the reliability and good predictably of neural network model in the prediction of membrane permeability of drugs.

scholarly journals A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

2021 ◽  
Author(s):  
Tobias Gensch ◽  
Gabriel dos Passos Gomes ◽  
Pascal Friederich ◽  
Ellyn Peters ◽  
Theophile Gaudin ◽  
...  
Keyword(s):  
Structure Property ◽  
Vast Number ◽  
Structure Property Relationships ◽  
Reaction Optimization ◽  
Physicochemical Descriptors ◽  
Mechanical Methods ◽  
Quantitative Structure Property Relationships ◽  
Property Space ◽  
Novel Catalyst ◽  
Molecular Catalysts

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

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