Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)

2019 ◽  
Vol 22 (5) ◽  
pp. 333-345
Author(s):  
Morteza Rezaei ◽  
Esmat Mohammadinasab ◽  
Tahere Momeni Esfahani
Keyword(s):  
Density Functional ◽  
Pharmaceutical Compounds ◽  
Partial Least Square ◽  
Least Square ◽  
Basis Set ◽  
Van Der Waals Volume ◽  
Relationship Analysis ◽  
Aniline Derivatives ◽  
Aqueous Solvent ◽  
Qsar Models

Background: In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds. Objective: The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models. Materials & Methods: Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds. Results: It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models. Conclusion: In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.

scholarly journals Potential Functions and Thermodynamic Properties of UC, UN, and UH

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Shuang-Ling Tang ◽  
Yu Wang ◽  
Qi-Ying Xia ◽  
Xue-Hai Ju
Keyword(s):  
Configuration Interaction ◽  
Density Functional ◽  
Least Square Method ◽  
Potential Functions ◽  
Least Square ◽  
Basis Set ◽  
Dissociation Energies ◽  
Anharmonicity Constant ◽  
Different Temperatures ◽  
Relationship Of

Potential energy surface scanning for UC, UN, and UH was performed by configuration interaction (CI), coupled cluster singles and doubles (CCSD) excitation, quadratic configuration interaction (QCISD (T)), and density functional theory PBE1 (DFT-PBE1) methods in coupling with the ECP80MWB_AVQZ + 2f basis set for uranium and 6 − 311 + G∗ for carbon, hydrogen, and nitrogen. The dissociation energies of UC, UN, and UH are 5.7960, 4.5077, and 2.6999 eV at the QCISD (T) levels, respectively. The calculated energy was fitted to the potential functions of Morse, Lennard-Jones, and Rydberg by using the least square method. The anharmonicity constant of UC is 0.0047160. The anharmonic frequency of UC is 780.27 cm−1 which was obtained based on the PBE1 results. For UN, the anharmonicity constant is 0.0049827. The anharmonic frequency is 812.65 cm−1 which was obtained through the PBE1 results. For UH, the anharmonicity constant is 0.017300. The anharmonic frequency obtained via the QCISD (T) results is 1449.8 cm−1. The heat capacity and entropy in different temperatures were calculated using anharmonic frequencies. These properties are in good accordance with the direct DFT-UPBE1 results (for UC and UN) and QCISD (T) results (for UH). The relationship of entropy with temperature was established.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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.

QSAR Analysis of Saquinavir Analogues (Peptidomimetics) as Potent HIV‐Protease Inhibitors

2012 ◽  
Vol 2 (3) ◽  
pp. 118-127
Author(s):  
Vandana Saini ◽  
Ajit Kumar
Keyword(s):  
Biological Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Partial Least Square ◽  
Least Square ◽  
Activity Prediction ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Physicochemical Descriptors ◽  
Qsar Models

The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D‐ and 3D‐ Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIV‐protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), k‐nearest neighbour (k‐NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2D‐QSAR analysis. In case of 3D‐QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3D‐QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIV‐protease. Also, the QSAR models generated can be very useful to predict the HIV‐PIs and also for virtual screening for identification of new lead molecules.

scholarly journals QSAR studies on imidazoles and sulfonamides as antidiabetic agents

2019 ◽  
Vol 30 (1) ◽  
pp. 5-13
Author(s):  
Veerasamy Ravichandran ◽  
Rajak Harish
Keyword(s):  
Predictive Power ◽  
Partial Least Square ◽  
Least Square ◽  
Antidiabetic Activity ◽  
Statistical Parameters ◽  
Qsar Studies ◽  
2D Qsar ◽  
Partial Least Square Analysis ◽  
Qsar Models ◽  
The Relationship

Abstract The main objective of the present study was to establish significant and validated QSAR models for imidazoles and sulfonamides to explore the relationship between their physicochemical properties and antidiabetic activity. Two dimensional QSAR models had been developed by multiple linear regression and partial least square analysis methods, and then validated for internal and external predictions. The established 2D QSAR models were statistically significant and highly predictive. The validation methods provided significant statistical parameters with q2 > 0.5 and pred_r2 > 0.6, which proved the predictive power of the models. The developed 2D QSAR models revealed the significance of SlogP and T_N_O_5, and Mol.Wt and SsBrE-index properties of imidazoles and sulfonamides on their antidiabetic activity, respectively. These results should prove to be an essential guide for the further design and development of new imidazoles and sulfonamides having better antidiabetic activity.

3D-QSAR Studies on 4,5-Dihydro-1H-pyrazolo [4,3-h] Quinazolines as Plk-1, CDK2/A and Aur-A Serine/Threonine Kinase Inhibitors

2020 ◽  
Vol 17 (4) ◽  
pp. 388-395
Author(s):  
Bhoomendra A. Bhongade ◽  
Nikhil D. Amnerkar ◽  
Andanappa K. Gadad
Keyword(s):  
Protein Kinases ◽  
Proliferative Activity ◽  
Ovarian Cancer Cell Line ◽  
3D Qsar ◽  
Partial Least Square ◽  
Least Square ◽  
Cell Cycle Checkpoints ◽  
Hydrogen Bond Donor ◽  
Qsar Studies ◽  
Qsar Models

Background: The family of serine/threonine protein kinases is associated with peculiar tumor cell-cycle checkpoints which are overexpressed in proliferating tissues as well as in cancers, making them as potential targets for cancer chemotherapy. In the present paper, 3D-QSAR studies were carried out on 4,5-dihydro-1H-pyrazolo[4,3-h]quinazolines against serine/threonine protein kinases viz. polo-like 1 (Plk-1), cyclin dependent 2/A (CDK2/A) and Aurora-A (Aur-A) and their in vitro anti-proliferative activity on A2780 ovarian cancer cell line. Methods: 3D-QSAR models were derived using stepwise forward-backward partial least square (SWFB_PLS) regression method using VlifeMDS QSAR plus software and the docking calculations were carried out using Docking Server. Results: The derived statistically significant and predictive 3D-QSAR models exhibited correlation coefficient r2 in the range of 0.875 to 0.966 and predictive r2 in the range of 0.492 to 0.618. The hydrogen bond donor NH group joining the phenyl ring with quinazoline and terminal amide group were found to favored for Plk-1, CDK2/A and anti-proliferative activity. Estimated energy of binding of compound 45 with enzymes was in the range of -8.52 to -9.03. Conclusion: The results of 3D-QSAR studies may be useful in the development of new pyrazolo[ 4,3-h]quinazoline derivatives with better inhibitory activities against serine/threonine kinases.

A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (04) ◽  
pp. 22-31
Author(s):  
M. C Sharma ◽  
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Least Square ◽  
Qsar Study ◽  
Partial Least Square Analysis ◽  
Qsar Models

A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.

scholarly journals A study of new antimalarial artemisinins through molecular modeling and multivariate analysis

2010 ◽  
Vol 75 (11) ◽  
pp. 1533-1548 ◽  
Author(s):  
João Ferreira ◽  
Antonio Figueiredo ◽  
Jardel Barbosa ◽  
Maria Cristino ◽  
Williams Macedo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Antimalarial Activity ◽  
External Validation ◽  
Geometry Optimization ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Basis Set ◽  
Validation Parameters

Artemisinin and 18 derivatives with antimalarial activity against W-2 strains of Plasmodium falciparum were studied through quantum chemistry and multivariate analysis. The geometry optimization of the structures was realized with the Hartree-Fock (HF) theory and 3-21G basis set. Maps of molecular electrostatic potential (MEP) and molecular docking were used to investigate the interaction between the ligands and the receptor (heme). Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were employed to select the most important descriptors related to activity. A predictive model was generated by the Partial Least Square (PLS) method through 15 molecules and 4 used as an external validation set, which were selected in the training set, the validation parameters of which are Q2 = 0.85 and R2 = 0.86. The model included as molecular parameters, the radial distribution function, RDF060e, the hydration energy, HE, and the distance between the O1 atom from the ligand and the iron atom from heme, d(Fe-O1). Thus, the synthesis of new derivatives may follow the results of the MEP maps and the PLS analysis.

scholarly journals Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents

2017 ◽  
pp. 117-126
Author(s):  
Milica Karadzic ◽  
Strahinja Kovacevic ◽  
Lidija Jevric ◽  
Sanja Podunavac-Kuzmanovic
Keyword(s):  
Antifungal Activity ◽  
Antifungal Agents ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Linear Modeling ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models

Quantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the principal component analysis (PCA). The partial least square regression (PLSR) was used to distinguish the most important molecular descriptors for non-linear modeling. Artificial neural networks (ANNs) were applied for the antifungal activity prediction. The best QSAR models were validated by statistical parameters and graphical methods. High agreement between the observed and predicted antifungal activity values indicated the good quality of the derived QSAR models. The obtained QSAR-ANN models can be used to predict the antifungal activity of dihydroindeno and indeno thiadiazines and of structurally similar compounds. The modeling of the antifungal activity can contribute to the synthesis of new antifungal agents with better ability to protect food and feed from the mycotoxins.

scholarly journals Peranan Sistem Agribisnis terhadap Keberhasilan Tumpangsari Cabai-Tembakau (Kasus Subak Di Desa Sukawati, Kecamatan Sukawati, Kabupaten Gianyar)

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Surata Adnyana ◽  
I Made Narke Tenaya ◽  
Dwi Putra Darmawan
Keyword(s):  
Informal Education ◽  
Agricultural Development ◽  
Simple Random Sampling ◽  
Farming System ◽  
Partial Least Square ◽  
Least Square ◽  
Relationship Analysis ◽  
Village Government ◽  
Quantitative Descriptive ◽  
Field Schools

ABSTRACTThe Role of System Agribusiness On Intercropping Success Of Chili-Tobacco(Subak Case in Sukawati Village, Sukawati Sub-District,Gianyar District)Agricultural development in Indonesia is still the mainstay and the mostimportant sectors of the overall economy. One of the way, developing the potential ofregions that still exist through horticulture and folk plantation and implement thecorrect agribusiness management system. Agribusiness as a sector of the folk economystill have bright prospects for further development, both to reinforce the folk's economyas well as Indonesia state devisa.Sukawati village very potential to develop the chili-tobacco intercroppingsystem because it has the most suitable land and widest in Gianyar district. Chilitobaccoplants have the same genus, can grow well, have local varieties that still exist,tobacco be grown are tobacco of the folk plantations who are used to the fringe.The study was conducted in the Sukawati Village that be selected purposively. Thestudy population was all the farmers who have applied intercropping farming system ofchilli-tobacco in 2013 and 2014 in 13 Subak in the Sukawati Village. The researchsample amount 90 respondents were selected by simple random sampling method. Thedata were analyzed using quantitative descriptive method and relationship analysis withthe partial least square (PLS) with help Smart PLS program.The analysis showed that are: (1) sub-systems procurement and distribution ofproduction facilities contribute positive and significant toward the intercroppingsuccess of chilli-tobacco with good category; (2) sub-systems of farming contributepositive and significant toward the intercropping success of chilli-tobacco with goodcategory; (3) sub-system of post-harvest and processing advanced contribute positiveand significant toward the intercropping success of chilli-tobacco with good category;(4) sub-system marketing contribute positive and significant to the intercroppingsuccess of chilli-tobacco with less good category; and (5) sub-system supportingservices contribute positive and significant toward the intercropping success of chillitobaccowith good category. Suggestions can be submitted are: (1) informal education of farmers in the formof field schools and training should be improved; (2) the farmer groups that have formed a cooperative to business of production facilities only fertilizers need tocooperate with village government to form village-owned enterprises (BUMDES) inorder to able to market the chilli-tobacco so that price stability can be maintained; and(3) other studies with the model and different variables need to be done further.Key words: role, agribusiness system, intercropping success

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