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.

Permeability prediction for zwitterions via chromatographic indexes and classification into ‘certain’ and ‘uncertain’

2019 ◽  
Vol 11 (13) ◽  
pp. 1553-1563 ◽  
Author(s):  
Francesco Caudana ◽  
Giuseppe Ermondi ◽  
Maura Vallaro ◽  
Marina Shalaeva ◽  
Giulia Caron
Keyword(s):  
Operating Characteristic ◽  
Ad Hoc ◽  
Roc Curves ◽  
Reversed Phase ◽  
Structure Property ◽  
Artificial Membrane ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Multiple Species ◽  
Ph Range

Background: The development of zwitterions to a drug is likely to be more challenging than compounds of other charge types. Results: Two chromatographic indexes (log k′80 PLRP-S and log KWIAM) can be successfully used as permeability classifiers of ampholytes. Moreover, a pragmatic classification into ordinary ampholytes; zwitterions ‘certain’ (i.e., the zwitterionic species is dominant in the physiological pH range); and zwitterions ‘uncertain’ (multiple species are present in the physiological pH range) enables to study the permeability of ampholytic compounds in relation to species distribution. Methodology: Potentiometry (pKa), reversed-phase (RP)-chromatography, tri-layer parallel artificial membrane permeability assays, quantitative structure-property relationships (QSPR) and block relevance (BR) analysis, receiver operating characteristic (ROC) curves. Conclusion: Structures considered as poorly permeable like zwitterions can be integrated in drug discovery programs by applying ad hoc experimental and computational tools.

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

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.

Characteristic study of irregularity measures of some nanotubes

2019 ◽  
Vol 97 (10) ◽  
pp. 1125-1132 ◽  
Author(s):  
Zahid Iqbal ◽  
Adnan Aslam ◽  
Muhammad Ishaq ◽  
Muhammad Aamir
Keyword(s):  
Quantitative Structure ◽  
Structure Property ◽  
Know How ◽  
Molecular Graphs ◽  
Irregularity Index ◽  
Structure Property Relationships ◽  
Boiling Points ◽  
Quantitative Structure Property Relationships ◽  
Quantitative Structure Activity Relationships ◽  
Vertex Degrees

In many applications and problems in material engineering and chemistry, it is valuable to know how irregular a given molecular structure is. Furthermore, measures of the irregularity of underlying molecular graphs could be helpful for quantitative structure property relationships and quantitative structure-activity relationships studies, and for determining and expressing chemical and physical properties, such as toxicity, resistance, and melting and boiling points. Here we explore the following three irregularity measures: the irregularity index by Albertson, the total irregularity, and the variance of vertex degrees. Using graph structural analysis and derivation, we compute the above-mentioned irregularity measures of several molecular graphs of nanotubes.

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