scholarly journals Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 922 ◽  
Author(s):  
Piotr Cysewski ◽  
Maciej Przybyłek
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Binding Constants ◽  
Quantitative Structure Activity Relationship ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Equations ◽  
Class Iii ◽  
Qspr Model ◽  
Beta Cyclodextrin ◽  
Standard Application

The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).

scholarly journals MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
María Jimena Martínez ◽  
Marina Razuc ◽  
Ignacio Ponzoni
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Software Tool ◽  
Quantitative Structure Activity Relationship ◽  
Second Phase ◽  
Qsar Studies ◽  
Qsar Modelling ◽  
Evolutionary Technique ◽  
Two Phases ◽  
Selection Of

The selection of the most relevant molecular descriptors to describe a target variable in the context of QSAR (Quantitative Structure-Activity Relationship) modelling is a challenging combinatorial optimization problem. In this paper, a novel software tool for addressing this task in the context of regression and classification modelling is presented. The methodology that implements the tool is organized into two phases. The first phase uses a multiobjective evolutionary technique to perform the selection of subsets of descriptors. The second phase performs an external validation of the chosen descriptors subsets in order to improve reliability. The tool functionalities have been illustrated through a case study for the estimation of the ready biodegradation property as an example of classification QSAR modelling. The results obtained show the usefulness and potential of this novel software tool that aims to reduce the time and costs of development in the drug discovery process.

Multilayer Perceptron Model for Predicting Acute Toxicity of Fungicides on Rats

2018 ◽  
Vol 3 (1) ◽  
pp. 100-118 ◽  
Author(s):  
Mabrouk Hamadache ◽  
Abdeltif Amrane ◽  
Salah Hanini ◽  
Othmane Benkortbi
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Acute Oral Toxicity ◽  
Oral Toxicity ◽  
Structure Activity ◽  
Marquardt Algorithm ◽  
Qsar Models ◽  
Validation Set

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, a QSAR model based on 10 molecular descriptors to predict acute oral toxicity of 91 fungicides to rats was developed and validated. Good results (PRESS/SSY = 0.085 and VIF < 5) were obtained, showing the validation of descriptors in the obtained model. The best results were obtained with a 10/11/1 Artificial Neural Network model trained with the Levenberg-Marquardt algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext which was equal to 0.960. Accordingly, the model developed in this study provided excellent predictions and can be used to predict the acute oral toxicity of fungicides, particularly for those that have not been tested as well as new fungicides.

scholarly journals Investigation of 6-fluoroquinolones activity against Mycobacterium tuberculosis using theoretical molecular descriptors: a case study

2011 ◽  
Vol 9 (5) ◽  
pp. 855-866 ◽  
Author(s):  
Nikola Minovski ◽  
Aneta Jezierska-Mazzarello ◽  
Marjan Vračko ◽  
Tom Šolmajer
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Heuristic Method ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Model Parameters ◽  
Quantum Chemical Descriptors ◽  
Qsar Study ◽  
Validation Data ◽  
Data Set

AbstractA quantitative structure-activity relationship (QSAR) study on a set of 66 structurally-similar 6-fluoroquinolones was performed using a large pool of theoretical molecular descriptors. Ab initio geometry optimizations were carried out to reproduce the geometrical and electronic structure parameters. The resulting molecular structures were confirmed to be minima via harmonic frequency calculations. Obtained atomic charges, HOMO and LUMO energies, orbital electron densities, dipole moment, energy and many other properties served as quantum-chemical descriptors. A multiple linear regression (MLR) technique was applied to generate a linear model for predicting the biological activity, Minimal Inhibitory Concentration (MIC), treated as negative decade logarithm, (pMIC). The heuristic method was used to optimize the model parameters and select the most significant descriptors. The model was tested internally using the CV LOO procedure on the training set and validated against the external validation set. The result (Q 2 ext = 0.7393), which was obtained on an external, previously excluded validation data set, shows the predictive performances of this model (R 2tr = 0.7416, Q 2 tr = 0.6613) in establishing (Q)SAR of 6-fluoroquinolones. This validated model could be proficiently used to design new 6-fluoroquinolones with possible higher activity.

Predicting human skin absorption of chemicals: development of a novel quantitative structure activity relationship

2007 ◽  
Vol 23 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Wen Luo ◽  
Sarah Medrek ◽  
Jatin Misra ◽  
Gerhard J Nohynek
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Molecular Size ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Skin Absorption ◽  
Quantitative Structure ◽  
Efficacy Evaluation ◽  
Structure Activity

The objective of this study was to construct and validate a quantitative structure-activity relationship model for skin absorption. Such models are valuable tools for screening and prioritization in safety and efficacy evaluation, and risk assessment of drugs and chemicals. A database of 340 chemicals with percutaneous absorption was assembled. Two models were derived from the training set consisting 306 chemicals (90/10 random split). In addition to the experimental Kow values, over 300 2D and 3D atomic and molecular descriptors were analyzed using MDL's QsarIS computer program. Subsequently, the models were validated using both internal (leave-one-out) and external validation (test set) procedures. Using the stepwise regression analysis, three molecular descriptors were determined to have significant statistical correlation with Kp (R2 = 0.8225): logKow, ×0 (quantification of both molecular size and the degree of skeletal branching), and SsssCH (count of aromatic carbon groups). In conclusion, two models to estimate skin absorption were developed. When compared to other skin absorption QSAR models in the literature, our model incorporated more chemicals and explored a large number of descriptors. Additionally, our models are reasonably predictive and have met both internal and external statistical validations. Toxicology and Industrial Health 2007; 23: 39—45.

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm

2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Dihydroorotate Dehydrogenase ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Bee Algorithm ◽  
Test Sets ◽  
Leave One Out

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

Prediction of Oral Acute Toxicity of Organophosphates Using QSAR Methods

2019 ◽  
Vol 16 ◽  
Author(s):  
Mina Kianpour ◽  
Esmat Mohammadinasab ◽  
Tahereh Momeni Esfahani
Keyword(s):  
Acute Toxicity ◽  
Molecular Descriptors ◽  
Back Propagation ◽  
Quantitative Structure Activity Relationship ◽  
Average Deviation ◽  
Qsar Models ◽  
Ann Models ◽  
Similar Class ◽  
New Compounds ◽  
Oral Acute Toxicity

: The aim of the present study was to develop quantitative structure-activity relationship (QSAR) models, based on molecular descriptors to predict the oral acute toxicity (LD50) of organophosphate compounds. The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BP-ANN) methods were proposed. The prediction experiment showed that the BP-ANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BP-ANN models could well characterize the molecular structure of each compound. It was indicated that among molecular descriptors to predict the LD50 (mgkg-1) of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BP-ANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R2= 0.9999) and absolute average deviation (AAD=0.6981631) gave the best outcome, and the model predictions were in good agreement with experimental data. The proposed model may be useful for predicting LD50 (mgkg-1) of new compounds of similar class.

Design of Dual COX-2 and 5-LOX Inhibitors with Iron-Chelating Properties Using Structure-Based and Ligand-Based Methods

2021 ◽  
Vol 18 ◽  
Author(s):  
Jelena Bošković ◽  
Dušan Ružić ◽  
Olivera Čudina ◽  
Katarina Nikolic ◽  
Vladimir Dobričić
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Qsar Analysis ◽  
In Silico Admet ◽  
Cox 2 ◽  
Lox Inhibitors

Background: Inflammation is common pathogenesis of many diseases progression, such as malignancy, cardiovascular and rheumatic diseases. The inhibition of the synthesis of inflammatory mediators by modulation of cyclooxygenase (COX) and lipoxygenase (LOX) pathways provides a challenging strategy for the development of more effective drugs. Objective: The aim of this study was to design dual COX-2 and 5-LOX inhibitors with iron-chelating properties using a combination of ligand-based (three-dimensional quantitative structure-activity relationship (3D-QSAR)) and structure-based (molecular docking) methods. Methods: The 3D-QSAR analysis was applied on a literature dataset consisting of 28 dual COX-2 and 5-LOX inhibitors in Pentacle software. The quality of developed COX-2 and 5-LOX 3D-QSAR models were evaluated by internal and external validation methods. The molecular docking analysis was performed in GOLD software, while selected ADMET properties were predicted in ADMET predictor software. Results: According to the molecular docking studies, the class of sulfohydroxamic acid analogues, previously designed by 3D-QSAR, was clustered as potential dual COX-2 and 5-LOX inhibitors with iron-chelating properties. Based on the 3D-QSAR and molecular docking, 1j, 1g, and 1l were selected as the most promising dual COX-2 and 5-LOX inhibitors. According to the in silico ADMET predictions, all compounds had an ADMET_Risk score less than 7 and a CYP_Risk score lower than 2.5. Designed compounds were not estimated as hERG inhibitors, and 1j had improved intrinsic solubility (8.704) in comparison to the dataset compounds (0.411-7.946). Conclusion: By combining 3D-QSAR and molecular docking, three compounds (1j, 1g, and 1l) are selected as the most promising designed dual COX-2 and 5-LOX inhibitors, for which good activity, as well as favourable ADMET properties and toxicity, are expected.

scholarly journals Quantitative Structure Activity Relationship (QSAR) Based on Electronic Descriptors and Docking Studies of Quinazoline Derivatives for Anticancer Activity

2018 ◽  
Vol 34 (5) ◽  
pp. 2361-2369
Author(s):  
Herlina Rasyid ◽  
Bambang Purwono ◽  
Ria Armunanto
Keyword(s):  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Docking Studies ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity ◽  
B3lyp Method ◽  
Quinazoline Derivatives

Quantitative structure-activity relationship (QSAR) based on electronic descriptors had been conducted on 2,3-dihydro-[1,4]dioxino[2,3-f]quinazoline analogues as anticancer using DFT/B3LYP method. The best QSAR equation described as follow: Log IC50 = -11.688 + (-35.522×qC6) + (-21.055×qC10) + (-85.682×qC12) + (-32.997×qO22) + (-85.129 EHOMO) + (19.724×ELUMO). Statistical value of R2 = 0.8732, rm2 = 0.7935, r2-r02/r2 = 0.0118, PRESS = 1.5727 and Fcalc/Ftable = 2.4067 used as external validation. Atomic net charge showed as the most important descriptor to predict activity and design new molecule. Following QSAR analysis, Lipinski rules was applied to filter the design compound due to physicochemical properties and resulted that all filtered compounds did not violate the rules. Docking analysis was conducted to determine interaction between proposed compounds and EGFR protein. Critical hydrogen bond was found in Met769 residue suggesting that proposed compounds could be used to inhibit EGFR protein.

scholarly journals Evaluation of Occlusal Contacts among Different Groups of Malocclusion using 3D Digital Models

2014 ◽  
Vol 15 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Naim Z Al-Rayes ◽  
Mohammad Y Hajeer
Keyword(s):  
Class Ii ◽  
Sexual Differences ◽  
Class I ◽  
Stepwise Multiple Regression ◽  
Regression Equations ◽  
Class Iii ◽  
Digital Models ◽  
Arch Width ◽  
Division 1 ◽  
3D Digital Models

ABSTRACT Objectives (1) To evaluate the applicability of using 3D digital models in the assessment of the magnitude of occlusal contacts by measuring occlusal contact surface areas (OCSAs) and 3D mesh points in ‘contact’ (OCMPs) in a sample of orthodontic patients; (2) To detect any sex differences in the magnitude of occlusal contacts in all malocclusion groups; (3) To detect intergroup differences; (4) To assess possible correlations between occlusal contacts and other dental characteristics. Materials and methods Study casts of 120 malocclusion patients were selected and divided into 4 groups (class I division 1, class II division 1, class II division 2, class III) with equal numbers for both sexes. 3D digital models were produced using O3DM™ technology. Occlusal contacts were quantified using two methods of measuring. Results (1) No significant sexual differences were detected for OCMPs (mesh points) and OCSAs (mm2) in all groups. (2) There were statistically significant differences among malocclusion groups for OCMPs and OCSAs (p < 0.001). Tukey's HSD posthoc tests showed that class III patients had significantly less occlusal contacts than other malocclusion groups. (3) Stepwise multiple regression equations showed that overjet, lower arch width and overbite could explain approximately 19.5% of the total variance of OCSAs and OCMPs. Conclusion Sexual differences in occlusal contacts were not detected. Class I division 1 patients had the highest amount of occlusal contacts among all groups of malocclusion. Overjet, overbite and lower dental arch width were best predictors of occlusal contacts in the current sample. How to cite this article Al-Rayes NZ, Hajeer MY. Evaluation of Occlusal Contacts among Different Groups of Malocclusion using 3D Digital Models. J Contemp Dent Pract 2014;15(1):46-55.

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.

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