GA-SMLR-Based QSAR Modeling and Molecular Docking Studies of Bisamidine Derivatives as NMT Inhibitors

2020 ◽  
Vol 5 (4) ◽  
pp. 1-31
Author(s):  
Sapna Jain Dabade ◽  
Dheeraj Mandloi ◽  
Amritlal V. Bajaj ◽  
Naveen Dhingra
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Qsar Model ◽  
Docking Studies ◽  
Qsar Modeling ◽  
Statistical Parameters ◽  
Van Der Waals Volume ◽  
Qsar Analysis ◽  
Hydrogen Bond Interactions

The present investigation deals with a combination of genetic algorithm-stepwise multiple linear regression (GA-SMLR)-based QSAR modeling and molecular docking applied to bisamidine analogues in an attempt to explore their role as novel NMT inhibitors of Candida albicans. In this regard, 43 bisamidine analogues were investigated for the development of mathematical models. The robustness of the proposed QSAR model was not only ascertained through traditionally used internal and external validation statistical parameters (Q2= 0.740, R2 = 0.819, R_Pred^2 = 0.636) but also through various R_(m)^2 metrics proposed by Roy and Mitra. The descriptors recognized in the QSAR analysis have culminated a significant role of atomic van der Waals volume, topology, nature of bond and dipole moment to modulate the antifungal activity of compounds under investigation. The most active compound revealed enhanced binding potency with MolDock score of -183.451 kcal/mol and displayed hydrogen bond interactions with active amino acids Leu177, Thr211, Tyr225, and IIe111 of NMT.

Molecular Docking and QSAR Studies of Coumarin Derivatives as NMT Inhibitors: Simple Structural Features as Potential Modulators of Antifungal Activity

2020 ◽  
Vol 17 (10) ◽  
pp. 1293-1308 ◽  
Author(s):  
Sapna Jain Dabade ◽  
Dheeraj Mandloi ◽  
Amritlal Bajaj
Keyword(s):  
Hydrogen Bonding ◽  
Molecular Docking ◽  
Amino Acid ◽  
Antifungal Agents ◽  
External Validation ◽  
Binding Pocket ◽  
Amino Acid Residues ◽  
Statistical Parameters ◽  
Van Der Waals Volume ◽  
Qsar Analysis

Background: Treatments of fungal diseases, including Candidiasis, remain not up to scratch in spite of the mounting catalog of synthetic antifungal agents. These have served as the impetus for investigating new antifungal agents based on natural products. Consequently, genetic algorithm-multiple linear regression (GA-MLR) based QSAR (Quantitative Structure-Activity Relationship) studies of coumarin analogues along with molecular docking were carried out. Methods: Coumarin analogues with their MIC values were used to generate the training and test sets of compounds for QSAR models development; the analogues were also docked into the binding pocket of NMT (MyristoylCoA: protein N-myristoyltransferase). Results and Discussion: The statistical parameters for internal and external validation of QSAR analysis (R2 = 0.830, Q2 = 0.758, R2Pred = 0.610 and R2m overall = 0.683 ), Y Randomization, Ridge trace, VIF, tolerance and model criteria of Golbraikh and Tropsha data illustrate the robustness of the best proposed QSAR model. Most of the analogues bind to the electrostatic, hydrophobic clamp and display hydrogen bonding with amino acid residues of NMT. Interestingly, the most active coumarin analogue (MolDock score of -189.257) was docked deeply within the binding pocket of NMT, thereby displaying hydrogen bonding with Tyr107, Leu451, Leu450, Gln226, Cys393 and Leu394 amino acid residues. Conclusion: The combinations of descriptors from various descriptor subsets in QSAR analysis have highlighted the role of atomic properties such as polarizability and atomic van der Waals volume to explain the inhibitory activity. The models and related information may pave the way for important insight into the designing of putative NMT inhibitors for Candida albicans.

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 New Insights into the Binding Features of F508del CFTR Potentiators: A Molecular Docking, Pharmacophore Mapping and QSAR Analysis Approach

2020 ◽  
Vol 13 (12) ◽  
pp. 445
Author(s):  
Giada Righetti ◽  
Monica Casale ◽  
Michele Tonelli ◽  
Nara Liessi ◽  
Paola Fossa ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Molecular Docking ◽  
Computational Study ◽  
Autosomal Recessive Disorder ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Van Der Waals Interactions ◽  
Transmembrane Conductance Regulator ◽  
Qsar Analysis

Cystic fibrosis (CF) is the autosomal recessive disorder most recurrent in Caucasian populations. To combat this disease, many life-prolonging therapies are required and deeply investigated, including the development of the so-called cystic fibrosis transmembrane conductance regulator (CFTR) modulators, such as correctors and potentiators. Combination therapy with the two series of drugs led to the approval of several multi-drug effective treatments, such as Orkambi, and to the recent promising evaluation of the triple-combination Elexacaftor-Tezacaftor-Ivacaftor. This scenario enlightened the effectiveness of the multi-drug approach to pave the way for the discovery of novel therapeutic agents to contrast CF. The recent X-crystallographic data about the human CFTR in complex with the well-known potentiator Ivacaftor (VX-770) opened the possibility to apply a computational study aimed to explore the key features involved in the potentiator binding. Herein, we discussed molecular docking studies performed onto the chemotypes so far discussed in the literature as CFTR potentiator, reporting the most relevant interactions responsible for their mechanism of action, involving Van der Waals interactions and π–π stacking with F236, Y304, F305 and F312, as well as H-bonding F931, Y304, S308 and R933. This kind of positioning will stabilize the effective potentiator at the CFTR channel. These data have been accompanied by pharmacophore analyses, which promoted the design of novel derivatives endowed with a main (hetero)aromatic core connected to proper substituents, featuring H-bonding moieties. A highly predictive quantitative-structure activity relationship (QSAR) model has been developed, giving a cross-validated r2 (r2cv) = 0.74, a non-cross validated r2 (r2ncv) = 0.90, root mean square error (RMSE) = 0.347, and a test set r2 (r2pred) = 0.86. On the whole, the results are expected to gain useful information to guide the further development and optimization of new CFTR potentiators.

scholarly journals Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

2020 ◽  
Vol 11 (1) ◽  
pp. 60-67
Author(s):  
Shola Elijah Adeniji ◽  
Abdulwahab Isiaka ◽  
Kalen Ephraim Audu ◽  
Olajumoke Bosede Adalumo
Keyword(s):  
Molecular Docking ◽  
Correlation Coefficient ◽  
Drug Targets ◽  
Biological Activities ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Multi Drug Resistance ◽  
Validation Test

Emergence of multi-drug resistant strains of Mycobacterium tuberculosis to the available drugs has demanded for the development of more potent anti-tubercular agents with efficient pharmacological activities. Time consumed and expenses in discovering and synthesizing new drug targets with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis. To solve the above problem, Quantitative Structure Activity Relationship (QSAR) is a recent approach developed to discover a novel drug with a better biological against M. Tuberculosis. A validated QSAR model developed in this study to predict the biological activities of some anti-tubercular compounds and to design new hypothetical drugs is influenced with the molecular descriptors; AATS7s, VR1-Dzi, VR1-Dzs, SpMin7-Bhe and RDF110i. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.8875, adjusted correlation coefficient (R2adj) value of 0.8234 and leave one out cross validation coefficient (Qcv2) value of 0.8012 while the external validation test was found to have (R2test) of 0.7961 and Y-randomization Coefficient (cRp2) of 0.6832. Molecular docking shows that ligand 13 of 2,4-disubstituted quinoline derivatives have promising higher binding score of -18.8 kcal/mol compared to the recommended drugs; isoniazid -14.6 kcal/mol. The proposed QSAR model and molecular docking studies will provides valuable approach for the modification of the lead compound, designing and synthesis more potent anti-tubercular agents.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development

2020 ◽  
Vol 20 (14) ◽  
pp. 1389-1402 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Mladjan Golubović ◽  
Aleksandar M. Veselinović
Keyword(s):  
Monte Carlo ◽  
Molecular Docking ◽  
Computer Calculation ◽  
Molecular Graph ◽  
Optimization Method ◽  
Docking Studies ◽  
Optimization Approach ◽  
Qsar Modeling ◽  
Monte Carlo Optimization ◽  
Molecular Docking Studies

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

scholarly journals QSAR, molecular docking studies, ligand-based design and pharmacokinetic analysis on Maternal Embryonic Leucine Zipper Kinase (MELK) inhibitors as potential anti-triple-negative breast cancer (MDA-MB-231 cell line) drug compounds

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Hadiza Abdulrahman Lawal ◽  
Adamu Uzairu ◽  
Sani Uba
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Triple Negative Breast Cancer ◽  
Leucine Zipper ◽  
Triple Negative ◽  
Qsar Model ◽  
Docking Studies ◽  
Model Parameters ◽  
Standard Drug ◽  
Molecular Docking Studies

Abstract Background Cancer of the breast is known to be among the top spreading diseases on the globe. Triple-negative breast cancer is painstaking the most destructive type of mammary tumor because it spreads faster to other parts of the body, with high chances of early relapse and mortality. This research would aim at utilizing computational methods like quantitative structure–activity relationship (QSAR), performing molecular docking studies and again to further design new effective molecules using the QSAR model parameters and to analyze the pharmacokinetics “drug-likeliness” properties of the new compounds before they could proceed to pre-clinical trials. Results The QSAR model of the derivatives was highly robust as it also conforms to the least minimum requirement for QSAR model from the statistical assessments of (R2) = 0.6715, (R2adj) = 0.61920, (Q2) = 0.5460 and (R2pred) of 0.5304, and the model parameters (AATS6i and VR1_Dze) were used in designing new derivative compounds with higher potency. The molecular docking studies between the derivative compounds and Maternal Embryonic Leucine Zipper Kinase (MELK) protein target revealed that ligand 2, 9 and 17 had the highest binding affinities of − 9.3, − 9.3 and − 8.9 kcal/mol which was found to be higher than the standard drug adriamycin with − 7.8 kcal/mol. The pharmacokinetics analysis carried out on the newly designed compounds revealed that all the compounds passed the drug-likeness test and also the Lipinski rule of five. Conclusions The results obtained from the QSAR mathematical model of parthenolide derivatives were used in designing new derivatives compounds that were more effective and potent. The molecular docking result of parthenolide derivatives showed that compounds 2, 9 and 17 had higher docking scores than the standard drug adriamycin. The compounds would serve as the most promising inhibitors (MELK). Furthermore, the pharmacokinetics analysis carried out on the newly designed compounds revealed that all the compounds passed the drug-likeness test (ADME and other physicochemical properties) and they also adhered to the Lipinski rule of five. This gives a great breakthrough in medicine in finding the cure to triple-negative breast cancer (MBA-MD-231 cell line).

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

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