3D-QSAR Study on Toxicities of Substituted Phenols against Vibrio Qinghaiensis(Q67)

2012 ◽  
Vol 610-613 ◽  
pp. 607-611
Author(s):  
Ping Sun ◽  
Hui Liu ◽  
Guo Hua Zhao ◽  
Jun Tan ◽  
Fu Yang Wang
Keyword(s):  
Similarity Index ◽  
3D Qsar ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Substituted Phenols ◽  
Qsar Study ◽  
Prediction Ability ◽  
Comfa Model ◽  
Index Analysis ◽  
Qsar Models

To investigate the relationships between structures and toxicities of 16 substituted phenols against vibrio qinghaiensis (Q67), 3D-QSAR models were proposed by using comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA). The results suggest that the steric field of substituted group is the dominating factor for the toxicity. Two obtained models show fine stabilities and predictive abilities. Comaprably, the prediction ability of CoMFA model is slightly more advantageous than that of CoMSIA, which both can be used to predict the toxicity of these kinds of compounds, even to provide further theoretical guide about biological toxic mechanism of substituted phenols.

scholarly journals 3D-QSAR STUDY OF NEW ACETYL-COA:CHOLESTEROL O-ACYL TRANSFERASE (ACAT) INHIBITORS

2000 ◽  
Vol 68 (1) ◽  
pp. 65-73
Author(s):  
Salvatore Guccione ◽  
Filippo Russo ◽  
Thierry Langer
Keyword(s):  
Molecular Similarity ◽  
3D Qsar ◽  
Field Analysis ◽  
Similarity Analysis ◽  
Correct Prediction ◽  
Comparative Molecular Field Analysis ◽  
Acyl Transferase ◽  
Training Set ◽  
Qsar Study ◽  
Qsar Models

3D QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) were built on a training set of 19 previously described inhibitors of acetyl-CoA:cholesterol O-acyl transferase (ACAT) with a IC50 ranging from 47 nM to 200 µM. The models thus obtained were found to be predictive as shown by correct prediction of the inhibitory activity of a set of recently published compounds.

scholarly journals Design, Synthesis, Biological Evaluation, and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 387
Author(s):  
Xiangcong Wang ◽  
Moxuan Zhang ◽  
Ranran Zhu ◽  
Zhongshan Wu ◽  
Fanhong Wu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Modeling ◽  
3D Qsar ◽  
Binding Mode ◽  
Biological Evaluation ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Comfa Model ◽  
Qsar Models

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q2 = 0.797 and r2 = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q2 = 0.567 and r2 = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).

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.

scholarly journals In Silicon Design of Antimicrobial Oligopeptides Based on 3D-QSAR Modelling and Bioassay Evaluation

2021 ◽  
Author(s):  
Guangping Li ◽  
Yuxuan Wang ◽  
Yan Shen ◽  
Haiqiong Guo ◽  
Qingxiu He ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Therapeutic Option ◽  
Three Dimensional ◽  
Similarity Index ◽  
3D Qsar ◽  
Field Analysis ◽  
Multi Drug Resistance ◽  
E Coli ◽  
Qsar Models ◽  
Lower Drug

Abstract The emergence of multi-drug resistance bacteria poses great health theat. Therefore, it is a crucial demand to obtain new antibacterial drugs. Antimicrobial peptides (AMPs) have the characteristics of wide antimicrobial spectrum and lower drug resistance, hence, it is hopeful to substitute for classical antibiotics. In this study, two classic methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA), were used to analysis the structural feature of small AMPs against S. aureus or E. coli respectively. Subsequently, Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) models (for S. aureus, CoMFA: Q2 = 0.512, R2 = 0.943, F = 59.916; CoMSIA: Q2 = 0.645, R2 = 0.993, F = 339.242; for E. Coli, CoMFA: Q2 = 0.507, R2 = 0.913, F = 66.862; CoMSIA: Q2 = 0.573, R2 = 0.966, F = 96.84) with good predictability and stability were constructed. Seven novel small AMPs were designed and synthesized based on the theoretical model. The novel AMPs showed potent antibacterial activity against S. aureus and E. coli without causing host toxicity. Our findings provide a potential therapeutic option using 3D-QSAR models guiding the design and modification of novel AMPs, to address the prevalent infections caused by MDR bacterial.

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 of Sulfonamide Hydroxamates Derivatives as MMP-2 Inhibitors Topomer CoMFA and Molecular Docking

2020 ◽  
Vol 17 (11) ◽  
pp. 1364-1371
Author(s):  
Jian-Bo Tong ◽  
Feng Yi ◽  
Ding Luo ◽  
Tian-Hao Wang
Keyword(s):  
Molecular Docking ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Molecular Field ◽  
Comparative Molecular Field Analysis ◽  
Quantitative Structure ◽  
Prediction Ability ◽  
Topomer Comfa ◽  
Molecular Field Analysis

Background: In recent years, cancer has become the main cause of death and it is a serious threat to human health, so the development of new, selective and safe anticancer drugs is still the focus of medical research. Matrix metalloproteinases-2 (MMP-2) has been determined to play an important role in the regulation of tumor angiogenesis, which is closely related to the development of the tumor. Therefore, MMP-2 is considered as a promising target for tumor therapy. In this study, Tomper comparative molecular field analysis (Topomer CoMFA) and molecular docking were used to investigate the important role of sulfonamide hydroxamate derivatives, an inhibitor of MMP-2, in the inhibition of angiogenesis. Methods: Quantitative structure active relationship (QSAR) models of 35 sulfonamide hydroxamate derivatives with inhibitory MMPs were developed. The quantitative structure-activity relationship (QSAR) model was built by using Topomer comparative molecular field analysis (Topomer CoMFA) technique. Results and Conclusion: The results show that the cross-validated q2 value of the Topomer CoMFA model is 0.881 and the non-cross-validated r2 value is 0.967. The results show that the model is reasonable and reliable, and has good prediction ability. Molecular docking studies were used to find the actual conformations of chemicals in active sites of cancer protease, as well as the binding mode pattern to the binding site in MMP-2. The information provided by the 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 35 sulfonamide hydroxamate derivatives and help to design potential anti-cancer protease inhibitor molecules. Conclusion: Thirty-five analogs were used in the 3D-QSAR study. Topomer CoMFA 3D-QSAR method was used to build the model, and the model was well predicted and statistically validated. The results of 3D-QSAR and molecular docking analysis provide theoretical guidance for the synthesis of new MMP-2 inhibitors.

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