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.

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 Structural investigations of CXCR2 receptor antagonists by CoMFA, CoMSIA and flexible docking studies

2012 ◽  
Vol 62 (3) ◽  
pp. 287-304 ◽  
Author(s):  
Shravan Kumar Gunda ◽  
Rohith Kumar Anugolu ◽  
Sri Ramya Tata ◽  
Saikh Mahmood
Keyword(s):  
Standard Error ◽  
Three Dimensional ◽  
3D Qsar ◽  
Homology Model ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Structural Investigations ◽  
Qsar Analysis ◽  
Qsar Studies

= Three-dimensional quantitative structure activity relationship (3D QSAR) analysis was carried out on a et of 56 N,N’-diarylsquaramides, N,N’-diarylureas and diaminocyclobutenediones in order to understand their antagonistic activities against CXCR2. The studies included comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Models with good predictive abilities were generated with CoMFA q2 0.709, r2 (non-cross-validated square of correlation coefficient) = 0.951, F value = 139.903, r2 bs = 0.978 with five components, standard error of estimate = 0.144 and the CoMSIA q2 = 0.592, r2 = 0.955, F value = 122.399, r2 bs = 0.973 with six components, standard error of estimate = 0.141. In addition, a homology model of CXCR2 was used for docking based alignment of the compounds. The most active compound then served as a template for alignment of the remaining structures. Further, mapping of contours onto the active site validated each other in terms of residues involved with reference to the respective contours. This integrated molecular docking based alignment followed by 3D QSAR studies provided a further insight to support the structure-based design of CXCR2 antagonistic agents with improved activity profiles. Furthermore, in silico screening was adapted to the QSAR model in order to predict the structures of new, potentially active compounds.

3D-QSAR Study on Acute Toxicity of Halogenated Phenol to Green Fluorescent Protein Using CoMFA and CoMSIA

2013 ◽  
Vol 295-298 ◽  
pp. 95-99
Author(s):  
Hong Xia Liu ◽  
Guo Hua Zhao
Keyword(s):  
Acute Toxicity ◽  
Fluorescent Protein ◽  
3D Qsar ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Qsar Study ◽  
Qsar Studies ◽  
Halogenated Phenols ◽  
Similarity Indices ◽  
Green Fluorescent

3D-QSAR studies of halogenated phenols screening for acute toxicity were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Groups’ data has been modeled to obtain an average estimate and a predictive value for ranking and screening purposes. CoMFA and CoMSIA models have given cross-validation regression coefficient (q2) values of more than 0.80 and correlation coefficient (R2) value of more than 0. 96, which validated for their prediction, could be applied to predict unavailable data.

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.

QSAR Studies on Thiazole Derivatives as HCV NS5A Inhibitors via CoMFA and CoMSIA Methods

2019 ◽  
Vol 16 (4) ◽  
pp. 453-460 ◽  
Author(s):  
Jiayu Li ◽  
Wenyue Tian ◽  
Diaohui Gao ◽  
Yuying Li ◽  
Yiqun Chang ◽  
...  
Keyword(s):  
Nonstructural Protein ◽  
External Validation ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Thiazole Derivatives ◽  
Hydrogen Bond Acceptor ◽  
Qsar Studies ◽  
Comfa Model

Background: Hepatitis C Virus (HCV) infection is the major cause of hepatitis after transfusion. And HCV Nonstructural Protein 5A (NS5A) inhibitors have become a new hotspot in the study of HCV inhibitors due to their strong antiviral activity, rapid speed of viral removing and broad antiviral spectrum. Methods: Forty-five NS5A inhibitors were chosen to process three-dimensional quantitative structure- activity relationship (3D-QSAR) by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set consisting of 30 compounds was applied to establish the models and a test set consisting of 15 compounds was applied to do the external validation. Results: The CoMFA model predicted a q2 value of 0.607 and an r2 value of 0.934. And the CoMSIA model predicted a q2 value of 0.516 and an r2 value of 0.960 established on the effects of steric, electrostatic, hydrophobic and hydrogen-bond acceptor. 0.713 and 0.939 were the predictive correlation co-efficients (r2pred) of CoMFA and CoMSIA models, respectively. Conclusion: These conclusions provide a theoretical basis for drug design and screening of HCV NS5A complex inhibitors.

scholarly journals Biological enrichment prediction of polychlorinated biphenyls and novel molecular design based on 3D-QSAR/HQSAR associated with molecule docking

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Jiawen Yang ◽  
Wenwen Gu ◽  
Yu Li
Keyword(s):  
Partition Coefficients ◽  
Polychlorinated Biphenyl ◽  
Molecular Design ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Similarity Indices ◽  
Qsar Models ◽  
Molecule Docking

Abstract Based on the experimental data of octanol-water partition coefficients (Kow, represents bioaccumulation) for 13 polychlorinated biphenyl (PCB) congeners, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to establish 3D-QSAR models, combined with the hologram quantitative structure–activity relationship (HQSAR), the substitution sites (mono-substituted and bis-substituted) and substituent groups (electron-withdrawing hydrophobic groups) that significantly affect the octanol-water partition coefficients values of PCBs were identified, a total of 63 monosubstituted and bis-substituted were identified. Compared with using 3D-QSAR model alone, the coupling of 3D-QSAR and HQSAR models greatly increased the number of newly designed bis-substituted molecules, and the logKow reduction in newly designed bis-substituted molecules was larger than that of monosubstituted molecules. This was established to predict the Kow values of 196 additional PCBs and carry out a modification of target molecular PCB-207 to lower its Kow (biological enrichment) significantly, simultaneously maintaining the flame retardancy and insulativity after calculation by using Gaussian09. Simultaneously, molecular docking could further screen out three more environmental friendly low biological enrichment newly designed PCB-207 molecules (5-methyl-PCB-207, 5-amino-PCB-207, and 4-amino-5-ethyl-PCB-207).

scholarly journals CoMFA and CoMSIA Studies on Inhibitors of HIV-1 Integrase - Bicyclic Pyrimidinones

2010 ◽  
Vol 7 (s1) ◽  
pp. S75-S84 ◽  
Author(s):  
V. Radhika ◽  
S. Sree Kanth ◽  
M. Vijjulatha
Keyword(s):  
Biological Activities ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Integrase Inhibitors ◽  
Inhibitory Potency ◽  
Similarity Indices ◽  
Qsar Models ◽  
Hiv 1

To understand the structural requirements of HIV-1 integrase inhibitors and to design new ligands against human HIV-1 integrase with enhanced inhibitory potency, a 3D QSAR (quantitative structure-activity relationship) study with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a dataset of 35 bicyclic pyrimidinones which are inhibitors of human HIV-1 integrase was performed. QSAR models were computed with Sybyl. The 3D QSAR model showed very good statistical result, namely q2, r2and r2predvalues were high for both CoMFA and CoMSIA. Based on the high values for q2and r2we are confident that the 3D QSAR model gives good predictions that may be used to design better HIV-1 integrase inhibitors. The CoMFA and CoMSIA models reveal that steric and electrostatic fields contribute significantly with biological activities of the studied compounds.

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