scholarly journals Azole Compounds as Inhibitors of Candida albicans: QSAR Modelling

2021 ◽  
Vol 9 ◽  
Author(s):  
Davood Gheidari ◽  
Morteza Mehrdad ◽  
Mahboubeh Ghahremani
Keyword(s):  
Candida Albicans ◽  
Quantitative Structure Activity Relationship ◽  
Multidrug Resistant ◽  
Qsar Modeling ◽  
Data Set ◽  
Qsar Modelling ◽  
Leave One Out ◽  
Derivatives Of ◽  
Mlr Model

Candida albicans is a pathogenic opportunistic yeast found in the human gut flora. It may also live outside of the human body, causing diseases ranging from minor to deadly. Candida albicans begins as a budding yeast that can become hyphae in response to a variety of environmental or biological triggers. The hyphae form is responsible for the development of multidrug resistant biofilms, despite the fact that both forms have been associated to virulence Here, we have proposed a linear and SPA-linear quantitative structure activity relationship (QSAR) modeling and prediction of Candida albicans inhibitors. A data set that consisted of 60 derivatives of benzoxazoles, benzimidazoles, oxazolo (4, 5-b) pyridines have been used. In this study, that after applying the leverage analysis method to detect outliers’ molecules, the total number of these compounds reached 55. SPA-MLR model shows superiority over the multiple linear regressions (MLR) by accounting 90% of the Q2 of anti-fungus derivatives ‘activity. This paper focuses on investigating the role of SPA-MLR in developing model. The accuracy of SPA-MLR model was illustrated using leave-one-out (LOO). The mean effect of descriptors and sensitivity analysis show that RDF090u is the most important parameter affecting the as behavior of the inhibitors of Candida albicans.

scholarly journals A chemometric approach for prediction of antifungal activity of some benzoxazole derivatives against Candida albicans

2012 ◽  
pp. 273-282 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Lidija Jevric ◽  
Strahinja Kovacevic ◽  
Natasa Kalajdzija
Keyword(s):  
Candida Albicans ◽  
Antifungal Activity ◽  
Clinical Importance ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Parameters ◽  
Relationship Analysis ◽  
Calibration Models ◽  
Chemometric Approach ◽  
Leave One Out

The purpose of the article is to promote and facilitate prediction of antifungal activity of the investigated series of benzoxazoles against Candida albicans. The clinical importance of this investigation is to simplify design of new antifungal agents against the fungi which can cause serious illnesses in humans. Quantitative structure activity relationship analysis was applied on nineteen benzoxazole derivatives. A multiple linear regression (MLR) procedure was used to model the relationships between the molecular descriptors and the antifungal activity of benzoxazole derivatives. Two mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds against Candida albicans. The quality of the models was validated by the leave-one-out technique, as well as by the calculation of statistical parameters for the established model.

Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity

2019 ◽  
Vol 19 (11) ◽  
pp. 957-969 ◽  
Author(s):  
Ana Yisel Caballero-Alfonso ◽  
Maykel Cruz-Monteagudo ◽  
Eduardo Tejera ◽  
Emilio Benfenati ◽  
Fernanda Borges ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Computational Models ◽  
Antimalarial Activity ◽  
Majority Vote ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Modeling ◽  
Design Work ◽  
Data Set ◽  
Selection Of

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

scholarly journals QSAR modeling of antibacterial activity of some benzimidazole derivatives

2011 ◽  
Vol 17 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Benzimidazole Derivatives ◽  
Qsar Modeling ◽  
Qsar Study ◽  
Validation Procedure ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) study has been carried out for training set of 12 benzimidazole derivatives to correlate and predict the antibacterial activity of studied compounds against Gram-negative bacteria Pseudomonas aeruginosa. Multiple linear regression was used to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: parameter of lipophilicity (logP) and hydration energy (HE). Good agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the generated QSAR model.

scholarly journals Modeling the activity of 2-phenylnaphthalene inhibitors using self-training artificial neural networks

2010 ◽  
Vol 8 (4) ◽  
pp. 877-885 ◽  
Author(s):  
Zahra Garkani-Nejad ◽  
Naser Jalili-Jahani
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Quantitative Structure Activity Relationship ◽  
Multiple Linear Regressions ◽  
Quantitative Structure ◽  
Data Set ◽  
Structure Activity ◽  
Artificial Neural ◽  
Derivatives Of ◽  
Linear Regressions

AbstractThe present study investigates the quantitative structure-activity relationship (QSAR) of 2-phenylnaphthalene ligands on an estrogen receptor (ERα). A data set comprising 70 derivatives of 2-phenylnaphthalene is used. The most suitable parameters, classified as topological, geometric and electronic are selected using a combination of genetic algorithm and multiple linear regression (GA-MLR) methods. Then, selected descriptors are used as inputs for a self-training artificial neural network (STANN). Analysis of the results suggests that the STANN model shows superior results compared to the multiple linear regressions (MLR) by accounting for 91.0% of the variances of the antiseptic potency of the 2-phenylnaphthalene derivatives. The accuracy of the 8-4-1 STANN model is illustrated using leave-multiple-out (LMO) cross-validation and Y-randomization techniques.

Theoretical Investigations on the Role of Steroid-Skeleton C4 = C5 Unsaturation in Competitive Aromatase Inhibition

1989 ◽  
Vol 44 (3-4) ◽  
pp. 217-225 ◽  
Author(s):  
Martin Bohl ◽  
Zeno Simon ◽  
Jens Reiner Lochmann
Keyword(s):  
Double Bond ◽  
Competitive Inhibition ◽  
Quantitative Structure Activity Relationship ◽  
Hydroxyl Group ◽  
Theoretical Investigations ◽  
Derivatives Of ◽  
Ring Position ◽  
Steroid Skeleton ◽  
Good Agreement

Abstract A quantitative structure-activity relationship (QSAR ) approach by use of the minimal topological difference (MTD) method including 46 derivatives of 4-androstene-3,17-dione and 5a-androstane- 3,17-dione is applied to give indications about the role of the C 4 = C5 double bond in competitive inhibition of human placental aromatase and about sterical requirements in steroidaromatase interactions. The inhibitory activity is found to correlate with the sterical MTD variable, hydrophobicity, and π-system conjugation in the A,B-ring region. A comparison of the MTD results reveals a good agreement with interpretations based on freeenergy data derived from inhibition constants. By means of MM2 molecular mechanics and PCILO quantum-chemical calculations, the 4-ene structure is shown to significantly influence conformational features of C19 substituents which are important in enzym atic transformations. While 19-hydroxy-5a-androstane-3,17-dione favour a conformation having the hydroxyl group in the enzyme-directed out-of-ring position, the C 4 = C5 double bond energetically enables the steroid to adopt a conformation which can be hydroxylated without internal rotations. According to present theoretical findings, the 4-ene unsaturation thus exerts an indirect conformational influence by hydroxyl positioning appropriate to aromatase interactions and a direct electronic influence by π conjugation.

scholarly journals QSAR Modelling of Peptidomimetic Derivatives towards HKU4-CoV 3CLpro Inhibitors against MERS-CoV

Chemistry ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 391-401
Author(s):  
Imad Hammoudan ◽  
Soumaya Matchi ◽  
Mohamed Bakhouch ◽  
Salah Belaidi ◽  
Samir Chtita
Keyword(s):  
External Validation ◽  
Predictive Ability ◽  
Original Data ◽  
Predictive Capacity ◽  
Data Set ◽  
Qsar Modelling ◽  
Test Sets ◽  
Components Analysis ◽  
The Relationship ◽  
Mlr Model

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R2 = 0.691, cross-validation parameter Q2cv = 0.528 and the external validation parameter R2test = 0.794.

scholarly journals Molecular Modeling and Structure-Activity Relationship of Podophyllotoxin and Its Congeners

2010 ◽  
Vol 15 (5) ◽  
pp. 528-540 ◽  
Author(s):  
Pradeep Kumar Naik ◽  
Afroz Alam ◽  
Ashutosh Malhotra ◽  
Owasis Rizvi
Keyword(s):  
Cytotoxic Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Data Set ◽  
External Data ◽  
Structure Activity ◽  
Experimental Values ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) model has been developed between cytotoxic activity and structural properties by considering a data set of 119 podophyllotoxin analogs based on 2D and 3D structural descriptors. A systematic stepwise searching approach of zero tests, a missing value test, a simple correlation test, a multicollinearity test, and a genetic algorithm method of variable selection was used to generate the model. A statistically significant model ( r train2 = 0.906; q cv2 = 0.893) was obtained with the molecular descriptors. The robustness of the QSAR model was characterized by the values of the internal leave-one-out cross-validated regression coefficient ( q cv2) for the training set and r test2 for the test set. The overall root mean square error (RMSE) between the experimental and predicted pIC50 value was 0.265 and r test2 = 0.824, revealing good predictability of the QSAR model. For an external data set of 16 podophyllotoxin analogs, the QSAR model was able to predict the tubulin polymerization inhibition and mechanistically cytotoxic activity with an RMSE value of 0.295 in comparison to experimental values. The QSAR model developed in this study shall aid further design of novel potent podophyllotoxin derivatives.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

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