Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches

An indole ring system is considered as a versatile scaffold in the pharmaceutical field. In this article, comparative QSAR modeling (2D-QSAR, 3D-QSAR; kNN-MFA and CoMSIA) was performed on some Knoevenagel-type cytotoxic indole derivatives to understand the structural requirements for the cytotoxic property of these compounds. The 2D-QSAR model was statistically significant and imparted high predictive ability (nTrain = 30; R = 0.917; [Formula: see text] = 0.801; [Formula: see text] = 0.757; Q2 = 0.722; nTest = 9; [Formula: see text] = 0.799). A statistically significant 3D-QSAR kNN-MFA model (both with stepwise forward and simulated annealing model selection method) as well as a 3D-QSAR CoMSIA model was developed to identify the key chemical features associated with enhancing the cytotoxic activities of these indoles. The results suggest that the presence of bulky group in R position can cause better cytotoxic activities. Consequently, substitution with cyano group at X portion and cyano/ester/keto/sulphonyl features at Y position is favourable for the cytotoxicity. However, hydrophobic features in R′ region are unfavourable for the biological activity. The chemical and structural features identified from the study may provide important avenues to modulate the structure of these indoles to a desirable biological end point.

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Investigating Structural Requirements of Some Pyrimidine-linked Benzimidazole Derivatives as Anticancer Agents Against MCF-7 Cancerous Cell Line Through the use of 2D and 3D QSARs

Current Chemical Biology ◽

10.2174/2212796813666190207144407 ◽

2019 ◽

Vol 13 (3) ◽

pp. 232-249

Author(s):

Kale Mayura ◽

Khan Sharuk ◽

Hature Jyoti

Keyword(s):

Correlation Coefficient ◽

Anticancer Agents ◽

3D Qsar ◽

Qsar Model ◽

Benzimidazole Derivatives ◽

Linear Regression Method ◽

Core Nucleus ◽

Structural Requirements ◽

2D Qsar ◽

2D And 3D

Background: Cancer is an extremely fast, unrestrained and pathological propagation of cells. Yet there is no cancer treatment that is 100% efficient against scattered cancer. Heterocycles have been considered as a boon to treat several cancers of which pyrimidine is a core nucleus and holds an important place in cancer chemotherapy which is reflected in the use of drugs such as 5-fluorouracil, erlotinib, gefitinib and caneratinib. Also, many good antitumor active agents possess benzimidazoleas its core nucleus. Objective: To design novel pyrimidine-linked benzimidazoles and to explore their structural requirements related to anticancer potential. Methods: 2D and 3D Quantitative structure–activity relationship (QSAR) studies were carried out on a series of already synthesized 27 pyrimidine-benzimidazole derivatives. Results: Statistically significant and optimum 2D-QSAR model was developed by using step-wise variable multiple linear regression method, yielding correlation coefficient r2 = 0.89, cross-validated squared correlation coefficient q2 = 0.79 and external predictive ability of pred_r2 = 0.73 Best 3D-QSAR model was developed by employing molecular field analysis using step-wise variable k-nearest neighbor method which showed good correlative and predictive abilities in terms of q2 =0.77 and pred_r2= 0.93. Conclusion: These 2D and 3D models were found to give dependable indications which helped to optimize the pyrimidine-benzimidazole derivatives of the data set. The data yielded by 2D- QSAR and 3D-QSAR models will aid in giving better perceptions about structural requirements for developing newer anticancer agents.

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Identifying the Structural Features of Diphenyl Ether Analogues for InhA Inhibition: A 2D and 3D QSAR Based Study

Letters in Drug Design & Discovery ◽

10.2174/1570180816666190611153933 ◽

2019 ◽

Vol 17 (1) ◽

pp. 31-47 ◽

Cited By ~ 2

Author(s):

Ashutosh Prasad Tiwari ◽

Varadaraj Bhat Giliyar ◽

Gurypur Gautham Shenoy ◽

Vandana Kalwaja Eshwara

Keyword(s):

Inhibitory Activity ◽

Diphenyl Ether ◽

3D Qsar ◽

Qsar Model ◽

Structural Features ◽

Mycolic Acids ◽

2D Qsar ◽

Qsar Models ◽

Mycobacterial Cell Wall ◽

2D And 3D

Background: Enoyl acyl carrier protein reductase (InhA) is a validated target for Mycobacterium. It is an enzyme which is associated with the biosynthesis of mycolic acids in type II fatty acid synthase system. Mycobacterial cell wall majorly comprises mycolic acids, which are responsible for virulence of the microorganism. Several diphenyl ether derivatives have been known to be direct inhibitors of InhA. Objective: In the present work, a Quantitative Structure Activity Relationship (QSAR) study was performed to identify the structural features of diphenyl ether analogues which contribute to InhA inhibitory activity in a favourable way. Method: Both 2D and 3D QSAR models were built and compared. Several fingerprint based 2D QSAR models were generated and their relationship with the structural features was studied. Models which corroborated the inhibitory activity of the molecules with their structural features were selected and studied in detail. Results: A 2D-QSAR model, with dendritic fingerprints having regression coefficient, for test set molecules Q2 =0.8132 and for the training set molecules, R2 =0.9607 was obtained. Additionally, an atom-based 3D-QSAR model with Q2 =0.7697 and R2 =0.9159 was also constructed. Conclusion: The data reported by various models provides guidance for the designing of structurally new diphenyl ether inhibitors with potential activity against InhA of M. tuberculosis.

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Structural Investigations of Aroylindole Derivatives through 3D-QSAR and Multiple Pharmacophore Modeling for the Search of Novel Colchicines Inhibitor

Letters in Drug Design & Discovery ◽

10.2174/1570180817999200905092444 ◽

2020 ◽

Vol 17 ◽

Author(s):

Vijay K. Patel ◽

Harish Rajak

Keyword(s):

Virtual Screening ◽

Anticancer Agents ◽

Structural Alignment ◽

Pharmacophore Model ◽

3D Qsar ◽

Qsar Model ◽

Structural Features ◽

Pharmacophore Modeling ◽

Structural Investigations ◽

Pharmacophore Modelling

Background : The ligand and structure based integrated strategies are being repeatedly and effectively employed for the precise search and design of novel ligands against various disease targets. Aroylindole derivative have a similar structural analogy as Combretastatin A-4, and exhibited potent anticancer activity on several cancer cell lines. Objective: To identify structural features of aroylindole derivatives through 3D-QSAR and multiple pharmacophore modelling for the search of novel colchicines inhibitor via virtual screening. Method: The present study utilizes ligand and structure based methodology for the establishment of structure activity correlation among trimethoxyaroylindole derivatives and search of novel colchicines inhibitor via virtual screening. The 3DQSAR studies were performed using Phase module and provided details of relationship between structure and biological activity. A single ligand based pharmacophore model was generated from Phase on compound 3 and compound 29 and three energetically optimized structure based pharmacophore models were generated from e-pharmacophore for co-crystallized ligand, compound 3 and compound 29 with protein PBD ID 1SA0, 5EYP and 5LYJ. These pharmacophoric features containing hit-like compounds were collected from commercially available ZINC database and screened using virtual screening workflow. Results and Discussion: The 3D-QSAR model studies with good PLSs statistics for factor four was characterized by the best prediction coefficient Q2 (0.8122), regression R2 (0.9405), SD (0.2581), F (102.7), P (1.56e-015), RMSE (0.402), Stability (0.5411) and Pearson-r (0.9397). The generated e-pharmacophores have GH scores over 0.5 and AUAC ≥ 0.7 indicated that all the pharmacophores were suitable for pharmacophore-based virtual screening. The virtual screened compounds ZINC12323179, ZINC01642724, ZINC14238006 have showed similar structural alignment as co-crystallized ligand and showed the hydrogen bonding of ligand with ASN101, SER178, THR179, VAL238, CYS241 amino acid of protein. Conclusion: The study illustrates that the ligand and structure based pharmacophoric approach is beneficial for identification of structurally diverse hits, having better binding affinity on colchicines binding site as novel anticancer agents.

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Identification of the structural features of quinazoline derivatives as EGFR inhibitors using 3D-QSAR modeling, molecular docking, molecular dynamics simulations and free energy calculations

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2021.1956591 ◽

2021 ◽

pp. 1-16

Author(s):

Fangfang Wang ◽

Wei Yang ◽

Hongping Liu ◽

Bo Zhou

Keyword(s):

Molecular Dynamics ◽

Free Energy ◽

Molecular Docking ◽

Molecular Dynamics Simulations ◽

3D Qsar ◽

Structural Features ◽

Free Energy Calculations ◽

Qsar Modeling ◽

Quinazoline Derivatives ◽

Dynamics Simulations

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Design, synthesis, and biological evaluation of new 6,N2-diaryl-1,3,5-triazine-2,4-diamines as anticancer agents selectively targeting triple negative breast cancer cells

RSC Advances ◽

10.1039/d0ra04970k ◽

2020 ◽

Vol 10 (43) ◽

pp. 25517-25528

Author(s):

Ahmad Junaid ◽

Felicia Phei Lin Lim ◽

Edward R. T. Tiekink ◽

Anton V. Dolzhenko

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Anticancer Agents ◽

Breast Cancer Cells ◽

Triple Negative ◽

3D Qsar ◽

Qsar Model ◽

Biological Evaluation ◽

Design Synthesis ◽

Synthesis And Biological Evaluation

New highly potent and selective 6,N2-diaryl-1,3,5-triazine-2,4-diamines were designed and prepared using the 3D-QSAR model developed earlier.

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2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

BioMed Research International ◽

10.1155/2017/4649191 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 9

Author(s):

Manman Zhao ◽

Lin Wang ◽

Linfeng Zheng ◽

Mengying Zhang ◽

Chun Qiu ◽

...

Keyword(s):

Correlation Coefficient ◽

3D Qsar ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Structure Activity Relationship ◽

Egfr Inhibitors ◽

Activity Relationship ◽

Quantitative Structure ◽

2D Qsar ◽

Structure Activity

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2=0.565 (cross-validated correlation coefficient) and r2=0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.

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QSAR modeling of antibacterial activity of some benzimidazole derivatives

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq100405050p ◽

2011 ◽

Vol 17 (1) ◽

pp. 33-38 ◽

Cited By ~ 7

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.

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The Integration of Pharmacophore-Based 3D QSAR Modeling and Virtual Screening in Identification of Natural Product Inhibitors against SARS-CoV-2

Pharmaceutical Sciences ◽

10.34172/ps.2020.98 ◽

2020 ◽

Author(s):

Samira Norouzi ◽

Maryam Farahani ◽

Samad Nejad Ebrahimi

Keyword(s):

Virtual Screening ◽

3D Structure ◽

3D Qsar ◽

Qsar Model ◽

Docking Studies ◽

Molecular Characteristics ◽

Qsar Modeling ◽

Data Set ◽

Public Health Emergencies ◽

Current Outbreak

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.

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Combined 3D-QSAR and Molecular Docking Analysis of Thienopyrimidine Derivatives as Staphylococcus aureus Inhibitors

Acta Chimica Slovenica ◽

10.17344/acsi.2020.5985 ◽

2021 ◽

Vol 68 (2) ◽

pp. 289-303

Author(s):

Mebarka Ouassaf ◽

Salah Belaidi ◽

Saida Khamouli ◽

Houmam Belaidi ◽

Samir Chtita

Keyword(s):

Staphylococcus Aureus ◽

Molecular Docking ◽

3D Qsar ◽

Qsar Model ◽

Structural Features ◽

Positive Bacterium ◽

Docking Analysis ◽

Statistical Reliability ◽

Starting Point ◽

Effective Drugs

The discovery of antibacterials is considered one of the greatest medical achievements of all time. In this work, a combination of three computational analyzes: 3D-QSAR, molecular docking and ADME evaluation were applied in thienopyrimidine derivatives intended toward gram-positive bacterium Staphylococcus aureus. The validity of 3D-QSAR model was tested with a set of data which is divided into a training and a test set. The two models constructed (CoMFA and CoMSIA) show good statistical reliability (q2 = 0.758; r2 = 0.96; r2pred = 0.783) and (q2 = 0.744; r2 = 0.97; r2pred = 0.625) respectively. In addition, docking methods were applied to understand the structural features responsible for the affinity of the ligands in the binding of S. aureus DNA gyrase. Drug likeness and ADME analysis applied in this series of new proposed compounds, have shown that the five lead molecules would have the potential to be effective drugs and could be used as a starting point for designing compounds against Staphylococcus aureus.

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