QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions

Beheshti, Abolghasem; Pourbasheer, Eslam; Nekoei, Mehdi; Vahdani, Saadat

doi:10.1016/j.jscs.2012.07.019

Cited by 54 publications

(41 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process was repeated five times for each one of the five unique subsets selected at random. The overall mean for this process (20% full-leave-out cross-validation), Q 2 (L5o) = 0.7235 which indicates the robustness and stability of the constructed model (Beheshti et al, 2012).…”

Section: Internal Validationmentioning

confidence: 98%

“…Molecules in the training set are considered outlying in the X-space when their leverage exceeds the warning leverage (Beheshti et al, 2012).…”

Section: Applicability Domainmentioning

confidence: 99%

“…Figure 1 showed the Williams plot, the applicability domain is established inside a squared (Beheshti et al, 2012) area within ± 3 bound for residuals and a leverage threshold h* (Equation 4 above), where K is the number of model parameters and n is the number of compounds in the training set. It demonstrates that all the compounds of the training set and test set are inside of this square area with the exception of one compound.…”

Section: Applicability Domainmentioning

confidence: 99%

See 2 more Smart Citations

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

2016

View full text Add to dashboard Cite

Quantitative structure-activity relationship (QSAR) is based on the hypothesis that changes in molecular structure reflect changes in the observed response or biological activity. The success of any QSAR model depends on the accuracy of the input data, selection of appropriate descriptors, statistical tools, and the validation of the developed model. A suitable set of molecular descriptors were calculated to represent the molecular structures of compounds such as constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. The important descriptors were selected with the aid of the genetic function approximation technique. The obtained model was validated using R 2 cv = 0.700, LOF = 0.187, R 2 = 0.8085, R 2 adj = 0.7625, F = 17.586, RMSE = 0.1781 and SDEP = 0.098, R 2 pred = 0.7956, L 5o = 0.7235. Results showed that the predictive ability of the model was satisfactory and it can be used for designing similar group of antifungal compounds.Abduljelil Ajala is currently an MSc student working under the supervision of Adamu Uzairu and Idris O Suleiman. Specialize on theoretical chemistry, working on modeling of organic compounds using different software and statistical tools. This research proposed a model which may provide a better understanding of the antifungal activity of ketone analogous and use as guidance for proposition of new chemopreventive agents. PUBLIC INTEREST STATEMENTQuantitative structure-activity relationship (QSAR) models are essential of the in silico techniques. They are utilized to anticipate the properties of chemicals, considering the potential harmfulness of chemicals in the body and the earth. QSAR model includes measurably examining the current information for a scope of chemicals so as to recognize precisely which structural, physical, and chemical properties of the molecule best correlate with particular measured effects on organism and the environment. QSAR models are available and in use for some end points. As a rule, QSAR models are more solid in the light of the fact that the marvel to be demonstrated is less mindboggling and a huge number of experimental results are accessible in the information bases. Though for models to foresee endless impacts, the information are considerably more restricted and the marvel is much perplexing. It likewise helps in dissecting crude test information and confirms the test results.

show abstract

Section: Internal Validationmentioning

confidence: 98%

“…Molecules in the training set are considered outlying in the X-space when their leverage exceeds the warning leverage (Beheshti et al, 2012).…”

Section: Applicability Domainmentioning

confidence: 99%

Section: Applicability Domainmentioning

confidence: 99%

See 1 more Smart Citation

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

2016

View full text Add to dashboard Cite

show abstract

“…Both conformation as well as configuration of the molecules can be calculated by these descriptors by rotating them to align the principle moment of inertia with the x, y and z axes [38]. Dipole is the 3D electronic descriptor which indicates the behavior of the molecules in particular strength and its orientation [39].…”

Section: Qsarmentioning

confidence: 99%

Computational Design of Dengue Type-2 NS2B/NS3 Protease Inhibitor: 2D/3D QSAR of Quinoline and Its Molecular Docking

Hariono¹,

Kamarulzaman²,

Wahab³

2015

Proceedings of the 3rd International Conference on Computation for Science and Technology

View full text Add to dashboard Cite

Abstract:The reemergence of dengue outbreak demands an effective and efficient treatment especially for antiviral agent since neither vaccine nor drug is available to overcome this disease. This study designed the new model for Dengue Type-2 (DENV2) NS2B/NS3 protease inhibitor from quinoline scaffold by combining QSAR modeling with molecular docking. Genetic Function Approximation (GFA) method was used to construct the QSAR model showing good correlation coefficient (r 2 = 0.8996). The model was validated using Partial Least Square (PLS) analysis (r 2 = 0.861), Leave One Out -Cross Validation (LOO-CV with q 2 = 0.677) and external validation (r 2 = 0.854). The model was then used to design more quinoline derivatives by attaching more electron withdrawing group such as halogen and aldehyde. Ssearching for more diverse structure of protease inhibitors was carried out by screening out NCI database. Results showed that the QSAR model was able to predict the new protease inhibitor model with a nanomolar inhibition. The molecular docking was then performed to predict the binding mode of all protease inhibitor models and revealed that most of essential amino acid residues such as Ile36, His51, Ser135, Asp129, Tyr150, Gly151, Gly153 and Val154 significantly interact with the ligands. Based on the agreement between QSAR modeling and molecular docking results, the designed molecules were reported gaining insight activities as DENV2 NS2B/NS3 protease inhibitor.

show abstract

“…In another example, Behesti et al described developing a QSAR model for predicting antimalarial activity of urea derivatives using genetic algorithm-multiple regression (Behesti et al 2012). There were a set of 68 urea compounds containing alkyl-benzopyrazine substitution (1) and aryl indoline substitution (2) (Fig.…”

Section: Introductionmentioning

confidence: 99%

Innovative techniques to discover novel antimalarials

2015

View full text Add to dashboard Cite

Malaria a global pandemic has engulfed nearly 0.63 million people globally. It is high time that a cure for malaria is required to stop its ever increasing menace. Our commentary discusses the advent and contribution of genetic algorithm (GA) in the drug discovery efforts towards developing cure for malaria. GAs are computational models of Darwinian evolution, ideally capture and mimic the principles of genetic variation and natural selection to evolve good solutions through multiple iterations on the space of all possible candidate solutions, called the search space, to a given optimization problem. Herein we will discuss the applications, advantages, disadvantages and future directions of GA with respect to malaria.

show abstract

QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions

Cited by 54 publications

References 22 publications

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

Computational Design of Dengue Type-2 NS2B/NS3 Protease Inhibitor: 2D/3D QSAR of Quinoline and Its Molecular Docking

Innovative techniques to discover novel antimalarials

Contact Info

Product

Resources

About