3D-QSAR Studies on 4,5-Dihydro-1H-pyrazolo [4,3-h] Quinazolines as Plk-1, CDK2/A and Aur-A Serine/Threonine Kinase Inhibitors

2020 ◽  
Vol 17 (4) ◽  
pp. 388-395
Author(s):  
Bhoomendra A. Bhongade ◽  
Nikhil D. Amnerkar ◽  
Andanappa K. Gadad
Keyword(s):  
Protein Kinases ◽  
Proliferative Activity ◽  
Ovarian Cancer Cell Line ◽  
3D Qsar ◽  
Partial Least Square ◽  
Least Square ◽  
Cell Cycle Checkpoints ◽  
Hydrogen Bond Donor ◽  
Qsar Studies ◽  
Qsar Models

Background: The family of serine/threonine protein kinases is associated with peculiar tumor cell-cycle checkpoints which are overexpressed in proliferating tissues as well as in cancers, making them as potential targets for cancer chemotherapy. In the present paper, 3D-QSAR studies were carried out on 4,5-dihydro-1H-pyrazolo[4,3-h]quinazolines against serine/threonine protein kinases viz. polo-like 1 (Plk-1), cyclin dependent 2/A (CDK2/A) and Aurora-A (Aur-A) and their in vitro anti-proliferative activity on A2780 ovarian cancer cell line. Methods: 3D-QSAR models were derived using stepwise forward-backward partial least square (SWFB_PLS) regression method using VlifeMDS QSAR plus software and the docking calculations were carried out using Docking Server. Results: The derived statistically significant and predictive 3D-QSAR models exhibited correlation coefficient r2 in the range of 0.875 to 0.966 and predictive r2 in the range of 0.492 to 0.618. The hydrogen bond donor NH group joining the phenyl ring with quinazoline and terminal amide group were found to favored for Plk-1, CDK2/A and anti-proliferative activity. Estimated energy of binding of compound 45 with enzymes was in the range of -8.52 to -9.03. Conclusion: The results of 3D-QSAR studies may be useful in the development of new pyrazolo[ 4,3-h]quinazoline derivatives with better inhibitory activities against serine/threonine kinases.

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.

QSAR Analysis of Saquinavir Analogues (Peptidomimetics) as Potent HIV‐Protease Inhibitors

2012 ◽  
Vol 2 (3) ◽  
pp. 118-127
Author(s):  
Vandana Saini ◽  
Ajit Kumar
Keyword(s):  
Biological Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Partial Least Square ◽  
Least Square ◽  
Activity Prediction ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Physicochemical Descriptors ◽  
Qsar Models

The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D‐ and 3D‐ Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIV‐protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), k‐nearest neighbour (k‐NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2D‐QSAR analysis. In case of 3D‐QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3D‐QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIV‐protease. Also, the QSAR models generated can be very useful to predict the HIV‐PIs and also for virtual screening for identification of new lead molecules.

scholarly journals QSAR studies on imidazoles and sulfonamides as antidiabetic agents

2019 ◽  
Vol 30 (1) ◽  
pp. 5-13
Author(s):  
Veerasamy Ravichandran ◽  
Rajak Harish
Keyword(s):  
Predictive Power ◽  
Partial Least Square ◽  
Least Square ◽  
Antidiabetic Activity ◽  
Statistical Parameters ◽  
Qsar Studies ◽  
2D Qsar ◽  
Partial Least Square Analysis ◽  
Qsar Models ◽  
The Relationship

Abstract The main objective of the present study was to establish significant and validated QSAR models for imidazoles and sulfonamides to explore the relationship between their physicochemical properties and antidiabetic activity. Two dimensional QSAR models had been developed by multiple linear regression and partial least square analysis methods, and then validated for internal and external predictions. The established 2D QSAR models were statistically significant and highly predictive. The validation methods provided significant statistical parameters with q2 > 0.5 and pred_r2 > 0.6, which proved the predictive power of the models. The developed 2D QSAR models revealed the significance of SlogP and T_N_O_5, and Mol.Wt and SsBrE-index properties of imidazoles and sulfonamides on their antidiabetic activity, respectively. These results should prove to be an essential guide for the further design and development of new imidazoles and sulfonamides having better antidiabetic activity.

DEVELOPMENT OF A ROBUST QSAR MODEL OF ANGIOTENSIN RECEPTOR REVEALS A K NEAREST NEIGHBOR APPLICABLE TO DIVERSE SCAFFOLDS

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (06) ◽  
pp. 30-36
Author(s):  
M. C Sharma ◽  
◽  
D. V. Kohli
Keyword(s):  
Biological Activity ◽  
Nearest Neighbor ◽  
3D Qsar ◽  
Qsar Model ◽  
Partial Least Square ◽  
Least Square ◽  
Positive Contribution ◽  
K Nearest Neighbor ◽  
Qsar Studies ◽  
2D Qsar

Quantitative structure–activity relationship (QSAR) studies were performed on quinazolinone analogues for prediction of antihypertensive activity. The best significant 2D-QSAR model having r2 = 0.8118 and pred_r2 = 0.7428 was developed by stepwise-partial least square method. k-nearest neighbor molecular field analysis was used to construct the best 3D-QSAR model, showing good correlative and predictive capabilities in terms of q2 = 0.7388 and pred_r2 = 0.6983. Results reveal that the 2D-QSAR studies signify positive contribution of SssOE index and SsCH3 count towards the biological activity. The results have showed that electronegative groups are necessary for activity and halogen, bulky, less bulky groups in quinazolinones nucleus enhanced the biological activity. The information rendered by 2D- and 3D-QSAR models may lead to a better understanding of structural requirements of substituted quinazolinones derivatives and also aid in designing novel potent antihypertensive molecules.

Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)

2019 ◽  
Vol 22 (5) ◽  
pp. 333-345
Author(s):  
Morteza Rezaei ◽  
Esmat Mohammadinasab ◽  
Tahere Momeni Esfahani
Keyword(s):  
Density Functional ◽  
Pharmaceutical Compounds ◽  
Partial Least Square ◽  
Least Square ◽  
Basis Set ◽  
Van Der Waals Volume ◽  
Relationship Analysis ◽  
Aniline Derivatives ◽  
Aqueous Solvent ◽  
Qsar Models

Background: In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds. Objective: The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models. Materials & Methods: Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds. Results: It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models. Conclusion: In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.

2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

2017 ◽  
Vol 2 (1) ◽  
pp. 44-63
Author(s):  
Anacleto S. de Souza ◽  
Leonardo G. Ferreira ◽  
Adriano D. Andricopulo
Keyword(s):  
Neglected Tropical Diseases ◽  
Public Health Problem ◽  
Predictive Ability ◽  
3D Qsar ◽  
Structural Features ◽  
Field Analysis ◽  
Global Public Health ◽  
Qsar Studies ◽  
Qsar Models ◽  
2D And 3D

Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.

Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

2012 ◽  
Vol 90 (8) ◽  
pp. 675-692 ◽  
Author(s):  
Premlata K. Ambre ◽  
Raghuvir R. S. Pissurlenkar ◽  
Evans C. Coutinho ◽  
Radhakrishnan P. Iyer
Keyword(s):  
Hydrogen Bond ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1 ◽  
Qsar Models ◽  
Potential Inhibitors

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

scholarly journals 2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

2017 ◽  
pp. 956-977
Author(s):  
Anacleto S. de Souza ◽  
Leonardo G. Ferreira ◽  
Adriano D. Andricopulo
Keyword(s):  
Neglected Tropical Diseases ◽  
Public Health Problem ◽  
Predictive Ability ◽  
3D Qsar ◽  
Structural Features ◽  
Field Analysis ◽  
Global Public Health ◽  
Qsar Studies ◽  
Qsar Models ◽  
2D And 3D

Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.

LEAD IDENTIFICATION OF HYDROXAMATE DERIVATIVE AS SELECTIVE HDAC2 INHIBITOR USING COMPUTATIONAL APPROACHES

INDIAN DRUGS ◽  
2020 ◽  
Vol 57 (07) ◽  
pp. 26-39
Author(s):  
Divya Shirbhate E. ◽  
V.K. Patel ◽  
P. Patel ◽  
R. Veerasamy ◽  
T. Jawaid ◽  
...  
Keyword(s):  
Anticancer Agents ◽  
Factor Model ◽  
Hdac Inhibitors ◽  
Predictive Ability ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Partial Least Square ◽  
Least Square ◽  
Computational Method ◽  
Molecular Features

Histone deacetylase (HDAC) inhibitors have been established as a novel class of anticancer agents. The HDAC enzyme plays a vital role in gene transcription for regulation of cell proliferation, migration and apoptosis, immune pathways and angiogenesis. In this work, a series of 49 hydroxamate derivatives with available IC50 data were analyzed by computational method for the identification of leads. 3D-QSAR and pharmacophore modeling investigation were accomplished to identify the crucial pharmacophoric features and correlate 3D-chemical structure with HDAC inhibitory activity. The e-pharmacophore script and phase module were used for development of pharmacophore hypotheses, which characterized the 3D arrangement of molecular features necessary for the presence of biological activity. The 3D-QSAR analyses were carried out for five partial least square (PLS) factor model with excellent information and predictive ability, acquired R2 =0.9824, Q2 =0.8473 and with low standard deviation SD=0.2161. Molecular docking studies showed intermolecular interactions between small molecules and some amino acids, such as GLY140, Zn501, HIS132 and PHE 141 with good GlideScore as compared with that of vorinostat (SAHA).

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