A New Method for Predicting the Net Heat of Combustion of Organic Compounds

2013 ◽  
Vol 651 ◽  
pp. 210-215 ◽  
Author(s):  
Hong Yin Cao ◽  
Rui Wang
Keyword(s):  
Ant Colony Optimization ◽  
Organic Compounds ◽  
Molecular Descriptors ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Ant Colony ◽  
Heat Of Combustion ◽  
Structure Property ◽  
Qspr Model

A quantitative structure–property relationship (QSPR) model for prediction of standard net heat of combustion (ΔH0c) was developed based on the ant colony optimization (ACO) method coupled with the partial least square (PLS) for variable selection. For developing this model, a diverse set of 1650 organic compounds were used, and 1481 molecular descriptors were calculated for every compound. Four molecular descriptors were screened out as the parameters of the model, which was finally constructed using multi-linear regression (MLR) method. The squared correlation coefficient R2of the model was 0.995 for the training set of 1322 compounds. For the test set of 328 compounds, the corresponding R2was 0.996. The results of this study showed that an accurate prediction model for ΔH0ccould be obtained by using the ant colony optimization method. Moreover, this study can provide a new way for predicting the ΔH0cof organic compounds for engineering based on only their molecular structures.

The QSPR Study for the Net Heat of Combustion of Esters Based on Ant Colony Optimization

2014 ◽  
Vol 716-717 ◽  
pp. 180-183
Author(s):  
Hong Yin Cao ◽  
Rui Wang
Keyword(s):  
Ant Colony Optimization ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Ant Colony ◽  
Heat Of Combustion ◽  
Structure Property ◽  
Qspr Model ◽  
Reliable Model ◽  
Multi Linear Regression

A quantitative structure–property relationship (QSPR) model for predicting the standard net heat of combustion () was developed based on the ant colony optimization (ACO) method coupled with the partial least square (PLS) for variable selection. Five molecular descriptors were screened out as the parameters of the model, which were finally constructed using multi-linear regression (MLR) method. A reliable model of five parameters for predicting the of esters was established, which can provide some help for engineering to predict the based on only their molecular structures.

QSPR Study on Aqueous Solubility of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field

2011 ◽  
Vol 356-360 ◽  
pp. 95-100
Author(s):  
Kun Xie ◽  
Shu Qiao ◽  
Chuan Fu ◽  
Cong Cheng
Keyword(s):  
Three Dimensional ◽  
Aqueous Solubility ◽  
Partial Least Square ◽  
Least Square ◽  
Atomic Interaction ◽  
Structure Property ◽  
Qspr Model ◽  
Chemical Structures ◽  
Interaction Field ◽  
Leave One Out

A quantitative structure property relationship (QSPR) model is established for estimating aqueous solubility (log SW) of PCDD/Fs. Three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures, the correlation between the 3D-HoVAIF descriptors of PCDD/Fs and aqueous solubility (log SW) was established by partial least square (PLS) regression. The predictive power of the model was validated by leave-one-out cross-validated analysis. Moreover, the estimated values have been presented for those PCDD/Fs which are lack of experimentally data by the optimum model.

scholarly journals Study on QSPR Method for Theoretical Calculation of Boiling Point of Some organic Compounds

2013 ◽  
Vol 3 ◽  
pp. 93-95 ◽  
Author(s):  
Kamal Raj Sapkota
Keyword(s):  
Organic Compounds ◽  
Boiling Point ◽  
Linear Prediction ◽  
Molecular Descriptors ◽  
Molecular Structures ◽  
Structure Property ◽  
Structure Property Relationship ◽  
Forward Stepwise ◽  
Linear Prediction Model ◽  
Linear Regressions

Quantitative structure-property relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of boiling point using a set of 25 organic compounds. The molecular descriptors used to represent molecular structure include topological indices and constitutional descriptors. Forward stepwise regression was used to construct the QSPR models. Multiple linear regressions is utilized to construct the linear prediction model. The prediction result agrees well with the experimental value of these properties.The Himalayan PhysicsVol. 3, No. 3, July 2012Page: 93-95

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 QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

2014 ◽  
Vol 79 (9) ◽  
pp. 1111-1125 ◽  
Author(s):  
Dan-Dan Wang ◽  
Lin-Lin Feng ◽  
Guang-Yu He ◽  
Hai-Qun Chen
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Tetrahymena Pyriformis ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Qsar Studies ◽  
Qsar Models ◽  
Leave One Out

Quantitative structure-activity relationship (QSAR) models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS) was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl?Cl), Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR) method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944). Moreover, Y-scrambling test indicated there was no chance correlation in this model.

AN IMPROVED QSPR STUDY OF REVERSE FACTOR OF NANOPARTICLES IN ROADSIDE ATMOSPHERE ON KERNEL PARTIAL LEAST SQUARES AND GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1250106 ◽  
Author(s):  
ALI MEHDIKHANI ◽  
HAMID REZA LOTFIZADEH ◽  
KAMYAR ARMAN ◽  
HADI NOORIZADEH
Keyword(s):  
Genetic Algorithm ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Structure Property ◽  
Resolution Time ◽  
Kernel Partial Least Squares ◽  
Flight Mass Spectrometry ◽  
Structure Property Relationship ◽  
Test Sets

Thermal desorption-comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry (TD–GC × GC–HRTOF-MS) is one of the most powerful tools in analytical nanoparticle compounds. Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) models were used to investigate the correlation between reverse factor (RF) and descriptors for 50 nanoparticles fraction with a diameter of 29–58 nm in roadside atmosphere which obtained by TD–GC×GC–HRTOF-MS. The correlation coefficient leave-group-out cross validation (LGO-CV (Q2)) of prediction for the GA-PLS and GA-KPLS models for training and test sets were (0.761 and 0.718) and (0.825 and 0.814), respectively, revealing the reliability of these models. This is the first research on the quantitative structure-property relationship (QSPR) of the nanoparticles in roadside atmosphere using the GA-PLS and GA-KPLS.

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