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.

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.

3D-QSAR Studies of S-DABO Derivatives as Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

2019 ◽  
Vol 16 (8) ◽  
pp. 868-881
Author(s):  
Yueping Wang ◽  
Jie Chang ◽  
Jiangyuan Wang ◽  
Peng Zhong ◽  
Yufang Zhang ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Three Dimensional ◽  
Predictive Ability ◽  
3D Qsar ◽  
Binding Mode ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Reverse Transcriptase Inhibitors ◽  
Qsar Studies ◽  
Hiv 1

Background: S-dihydro-alkyloxy-benzyl-oxopyrimidines (S-DABOs) as non-nucleoside reverse transcriptase inhibitors have received considerable attention during the last decade due to their high potency against HIV-1. Methods: In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) of a series of 38 S-DABO analogues developed in our lab was studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The Docking/MMFF94s computational protocol based on the co-crystallized complex (PDB ID: 1RT2) was used to determine the most probable binding mode and to obtain reliable conformations for molecular alignment. Statistically significant CoMFA (q2=0.766 and r2=0.949) and CoMSIA (q2=0.827 and r2=0.974) models were generated using the training set of 30 compounds on the basis of hybrid docking-based and ligand-based alignment. Results: The predictive ability of CoMFA and CoMSIA models was further validated using a test set of eight compounds with predictive r2 pred values of 0.843 and 0.723, respectively. Conclusion: The information obtained from the 3D contour maps can be used in designing new SDABO derivatives with improved HIV-1 inhibitory activity.

scholarly journals 2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO-4-METHYL-6-PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DERIVATIVES AS FACTOR XA INHIBITOR

2019 ◽  
pp. 104-114
Author(s):  
Smita Suhane ◽  
A. G. Nerkar ◽  
Kumud Modi ◽  
Sanjay D. Sawant
Keyword(s):  
Factor Xa ◽  
3D Qsar ◽  
Qsar Model ◽  
Regression Method ◽  
Field Analysis ◽  
Factor Xa Inhibitors ◽  
Nearest Neighbour ◽  
Qsar Analysis ◽  
Qsar Models ◽  
2D And 3D

Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models. The models were developed for amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitors. Methods: With the help of Marvin application, 2D structures of thirty compounds of methaniminium derivatives were drawn and consequently converted to 3D structures. 2D QSAR using multiple linear regression (MLR) analysis and PLS regression method was performed with the help of molecular design suite VLife MDS 4.3.3. 3D QSAR analysis was carried out using k-Nearest Neighbour Molecular Field Analysis (k-NN-MFA). Results: The most significant 2D models of methaniminium derivatives calculated squared correlation coefficient value 0.8002 using multiple linear regression (MLR) analysis. Partial Least Square (PLS) regression method was also employed. The results of both the methods were compared. In 2D QSAR model, T_C_O_5, T_2_O_2, s log p, T_2_O_1 and T_2_O_6 descriptors were found significant. The best 3D QSAR model with k-Nearest Neighbour Molecular Field Analysis have predicted q2 value 0.8790, q2_se value 0.0794, pred r2 value 0.9340 and pred_r2 se value 0.0540. The stepwise regression method was employed for anticipating the inhibitory activity of this class of compound. The 3D model demonstrated that hydrophobic, electrostatic and steric descriptors exhibit a crucial role in determining the inhibitory activity of this class of compounds. Conclusion: The developed 2D and 3D QSAR models have shown good r2 and q2 values of 0.8002 and 0.8790 respectively. There is high agreement in inhibitory properties of experimental and predicted values, which suggests that derived QSAR models have good predicting properties. The contour plots of 3D QSAR (k-NN-MFA) method furnish additional information on the relationship between the structure of the compound and their inhibitory activities which can be employed to construct newer potent factor Xa inhibitors.

3D-QSAR STUDIES OF SUBSTITUTED 4-ARYL/HETEROARYL-4H-CHROMENES AS APOPTOSIS INDUCERS USING CoMFA AND CoMSIA

2009 ◽  
Vol 08 (01) ◽  
pp. 143-155 ◽  
Author(s):  
SI YAN LIAO ◽  
LI QIAN ◽  
TI FANG MIAO ◽  
YONG SHEN ◽  
KANG CHENG ZHENG
Keyword(s):  
Anticancer Activity ◽  
Predictive Ability ◽  
Decisive Role ◽  
3D Qsar ◽  
Field Analysis ◽  
Breast Cancer Cell Lines ◽  
Steric Interaction ◽  
Anticancer Activities ◽  
Strong Electron ◽  
Qsar Studies

Three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 36 apoptosis inducers, substituted 4-aryl/heteroaryl-4H-chromenes with anticancer activity against human breast cancer cell lines T47D, have been studied by using methods of comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA). The established 3D-QSAR models in training set show not only significant statistical quality, but also predictive ability, with high correlation coefficient (R2) values and cross-validation coefficient (q2) values: CoMFA (R2, q2: 0.944, 0.747), CoMSIA (R2, q2: 0.944, 0.704). Moreover, the predictive abilities of the CoMFA and CoMSIA models were further confirmed by a test set, giving the predictive correlation coefficients ([Formula: see text] values) of 0.845 and 0.851, respectively. Based on the CoMFA and CoMSIA contour map analyses, some key factors responsible for anticancer activity of this series of compounds have been found as follows: the steric interaction plays a decisive role in determining the anticancer activities of these compounds; bulky groups as substituent R 1 are not tolerated; in addition to a steric moderation, higher degree of electropositivity and hydrophobicity on the terminal alkyl of substituent R 2 might be favorable to the activity; the substituent R 3 should be hydrophobic; bulky and strong electron withdrawing groups for the substituent R 4 are not advantageous to the activity; simultaneously introducing large electronegative atoms as hydrogen-acceptors to the first atoms of the substituents R 5 and R 6 may increase the activity, but substituents R 5 and R 6 with a linking group – OCH 2 O – may decrease the activity. Such results can offer some useful theoretical references for understanding the action mechanism, designing more potent derivatives, and predicting their activities prior to synthesis.

Molecular Docking and 3D-QSAR Studies of 4-Phenylpiperidine Derivatives as μ-Opioid Agonists

2011 ◽  
Vol 361-363 ◽  
pp. 263-267 ◽  
Author(s):  
Ming Liu ◽  
Wen Xiang Hu ◽  
Xiao Li Liu
Keyword(s):  
Electrostatic Interactions ◽  
Biological Activities ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Qsar Studies ◽  
Chemical Structures ◽  
Opioid Agonists ◽  
Qsar Models ◽  
Leave One Out

A predictive 3D-QSAR model which correlates the biological activities with the chemical structures of a series of 4-phenylpiperidine derivatives as μ opioid agonists was developed by means of comparative molecular field analysis (CoMFA). The stabilities of the 3D-QSAR models were verified by the leave-one-out cross-validation method. Moreover, the predictive capabilities of the models were validated by an external test set. Best predictions were obtained with CoMFA standard model(q2=0.504, N=6, r2=0.968) which revealed how steric and electrostatic interactions contribute to agonists bioactivities, and provided us with important information to understand the interaction of agonists and μ opioid receptor .

COMPARISON OF THREE 3D-QSAR METHODS USING A NOVEL CLASS OF MURF INHIBITORS

2007 ◽  
Vol 06 (01) ◽  
pp. 63-80 ◽  
Author(s):  
DE-XIN KONG ◽  
WEI-LIANG ZHU ◽  
DA-LEI WU ◽  
XU SHEN ◽  
HUA-LIANG JIANG
Keyword(s):  
Cross Validation ◽  
Molecular Similarity ◽  
Predictive Ability ◽  
3D Qsar ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Virtual Database ◽  
Similarity Indices ◽  
Qsar Models ◽  
Good Agreement

MurF was considered as an attractive target for new antibacterial discovery. In this paper, three QSAR methods were employed, viz. comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram QSAR (HQSAR), to derive highly predictive QSAR models for designing novel MurF inhibitors and comparing different 3D-QSAR/alignment methods. QSAR models with high predictive ability for MurF inhibitors were successfully constructed in terms of cross-validation q2, standard error and predictive coefficient r2, which were around 0.70, 0.55 and 0.99, respectively. All the models from different methods were in good agreement with each other. Compounds with indeterminate activities were used as a test set; results showed that CoMSIA had the best predictive ability, followed by HQSAR and CoMFA. Based on these models, some key features for designing new MurF inhibitors were identified. A virtual database screen process was proposed based on the combination of these models.

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

2019 ◽  
Vol 17 (1) ◽  
pp. 31-47 ◽  
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.

scholarly journals 3D QSAR Studies of DAMNI Analogs as Possible Non-nucleoside Reverse Transcriptase Inhibitors

2008 ◽  
Vol 5 (s2) ◽  
pp. 1103-1113
Author(s):  
S. Ganguly ◽  
V. Gopalakrishnan
Keyword(s):  
Reverse Transcriptase ◽  
Minimum Energy ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
3D Qsar ◽  
Field Analysis ◽  
Template Molecule ◽  
Qsar Studies ◽  
Contour Maps ◽  
Nucleoside Inhibitors

The non-nucleoside inhibitors ofHIV-1-reverse transcriptase (NNRTIs) are an important class of drugs employed in antiviral therapy. Recently, a novel family ofNNRTIs commonly referred to as 1-[2-diarylmethoxy] ethyl) 2-methyl-5-nitroimidazoles (DAMNI) derivatives have been discovered. The 3D-QSARstudies onDAMNIderivatives asNNRTIs was performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The global minimum energy conformer of the template molecule 15, the most active molecule of the series, was obtained by simulated annealing method and used to build the structures of the molecules in the dataset. The combination of steric and electrostatic fields inCoMSIAgave the best results with cross-validated and conventional correlation coefficients of 0.654 and 0.928 respectively. The predictive ability ofCoMFAandCoMSIAwere determined using a test set of tenDAMNIderivatives giving predictive correlation coefficients of 0.92 and 0.98 respectively indicating good predictive power. Further, the robustness of the models was verified by bootstrapping analysis. The information obtained fromCoMFAandCoMSIA3Dcontour maps may be of utility in the design of more potentDAMNIanalogs asNNRTIs in future.

scholarly journals Receptor Guided 3D-QSAR: A Useful Approach for Designing of IGF-1R Inhibitors

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
M. Muddassar ◽  
F. A. Pasha ◽  
H. W. Chung ◽  
K. H. Yoo ◽  
C. H. Oh ◽  
...  
Keyword(s):  
Structural Information ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Qsar Studies ◽  
Similarity Indices ◽  
Qsar Models ◽  
Derivatives Of

Research by other investigators has established that insulin-like growth factor‐1 receptor (IGF-1R) is a key oncological target, and that derivatives of 1, 3-disubstituted-imidazo[1,5-] pyrazine are potent IGF-1R inhibitors. In this paper, we report on our three-dimensional quantitative structure activity relationship (3D-QSAR) studies for this series of compounds. We validated the 3D-QSAR models by the comparison of two major alignment schemes, namely, ligand-based (LB) and receptor-guided (RG) alignment schemes. The latter scheme yielded better 3D-QSAR models for both comparative molecular field analysis (CoMFA) (, ) and comparative molecular similarity indices analysis (CoMSIA) (, ). We submit that this might arise from the more accurate inhibitor alignment that results from using the structural information of the active site. We conclude that the receptor-guided 3D-QSAR may be helpful to design more potent IGF-1R inhibitors, as well as to understand their binding affinity with the receptor.

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