A 3D QSAR Study of Betulinic Acid Derivatives as Anti-Tumor Agents Using Topomer CoMFA: Model Building Studies and Experimental Verification

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.

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Application of an R-group search technique into molecular design of HIV-1 integrase inhibitors

Journal of the Serbian Chemical Society ◽

10.2298/jsc150826003t ◽

2016 ◽

Vol 81 (4) ◽

pp. 383-394 ◽

Cited By ~ 1

Author(s):

Jian-Bo Tong ◽

Min Bai ◽

Xiang Zhao

Keyword(s):

External Validation ◽

Multiple Correlation Coefficient ◽

Template Molecule ◽

Good Prediction ◽

Qsar Study ◽

Topomer Comfa ◽

Comfa Model ◽

Topomer Search ◽

New Compounds ◽

Hiv 1

In this paper, a three-dimensional quantitative structure-activity relationship (3D-QSAR) study for 62 HIV-1 integrase(IN) inhibitors was established using Topomer CoMFA. The multiple correlation coefficient of fitting, cross validation and external validation were 0.942, 0.670 and 0.748, respectively. The results indicated that the Topomer CoMFA model obtained has both favorable estimation stability and good prediction capability. Topomer Search was used to search R group from ZINC database. As the result, a series of R groups with relatively high activity contribution was obtained. By filtering with the most potent molecule in the set, 1 Ra group and 21 Rb groups were selected. We employed the 1 Ra groups and 21 Rb groups to alternately substitute the Ra and Rb of sample 42. Finally, we designed 21 new compounds and further predicted their activities using the Topomer CoMFA model and there were 10 new compounds with higher activity than that of the template molecule. The results suggested the Topomer Search technology could be effectively used to screen and design new HIV-1 IN inhibitors and has good predictive capability to guide the design of new HIV/AIDS drugs.

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A QSAR study of peptidyl vinyl sulfone cysteine protease inhibitors using Topomer CoMFA and Molecular Docking

Letters in Drug Design & Discovery ◽

10.2174/1570180818666210114115030 ◽

2021 ◽

Vol 18 ◽

Author(s):

WU Lu-Yang ◽

MA Yang-Min ◽

LEI Shan ◽

WANG Tian-Hao ◽

FENG Yi

Keyword(s):

Molecular Docking ◽

Protease Inhibitors ◽

Cysteine Protease ◽

Active Site ◽

3D Qsar ◽

Vinyl Sulfone ◽

Cysteine Protease Inhibitors ◽

Qsar Study ◽

Topomer Comfa ◽

Topomer Search

Background: Malaria is one of the most important infectious diseases in the world. The most severe form of malaria in humans is caused by Plasmodium falciparum. Malaria is a worldwide health problem, with 214 million new cases in 2015 and 438,000 deaths, most of which in Africa. Therefore, there is an urgent need for novel, low-toxic, more specific inhibitors to find new antimalarial agents. A promising target for antimalarial drug design is falcipain-2, a cysteine protease from P. falciparum, that has received considerable attention due to its key role in the life cycle of the parasite. Methods: Three-dimensional quantitative structure-activity relationship (3D-QSAR) models of 39 peptidyl vinyl sulfone cysteine protease inhibitors was constructed using Topomer CoMFA. Topomer Search was employed to virtually screen lead-like compounds in the ZINC database. Molecular docking was employed to further explore the binding requirements between the ligands and the receptor protein which included several hydrogen bonds between peptidyl vinyl sulfone cysteine protease inhibitors and active site residues. Results: The non-cross correlation coefficient (r 2 ), the interaction validation coefficient (q2 ) and the external validation (r 2 pred) were 0.902, 0.685 and 0.763, respectively. The results showed that the model not only had good estimation stability but also good prediction capability. 22 new molecules were obtained, whose predicted activity are higher than template molecules. The results showed that the Topomer Search technology can be effectively applied to screen and design new peptidyl vinyl sulfone cysteine protease inhibitors. Molecular docking showed extensive interactions between peptidyl vinyl sulfone cysteine protease inhibitors and residues of LYS24, ASP21, LYS59 and ASP17 in the active site. Conclusion: 39 peptidyl vinyl sulfone cysteine protease inhibitors were used in the 3D-QSAR study. Topomer CoMFA 3DQSAR method was used to build the model, and the model was well predicted and statistically validated. The design of potent new inhibitors of cysteine protease can get useful insights from these results.

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Application of an R-group search technique in the molecular design of dipeptidyl boronic acid proteasome inhibitors

Journal of the Serbian Chemical Society ◽

10.2298/jsc161227047t ◽

2017 ◽

Vol 82 (9) ◽

pp. 1025-1037 ◽

Cited By ~ 1

Author(s):

Jian-Bo Tong ◽

Yuan-Yuan Li ◽

Guo-Yan Jiang ◽

Kang-Nan Li

Keyword(s):

Boronic Acid ◽

Molecular Design ◽

External Validation ◽

Proteasome Inhibitors ◽

3D Qsar ◽

Multiple Correlation Coefficient ◽

Good Prediction ◽

Topomer Comfa ◽

Comfa Model ◽

Topomer Search

In this work, a 3D-QSAR model involving for 40 dipeptidyl boronic acid proteasome inhibitors was built based on Topomer CoMFA. The multiple correlation coefficient of fitting, cross-validation and external validation were 0.908, 0.647 and 0.703, respectively. The results indicated that the obtained Topomer CoMFA model has not only the favourable estimation stability but also the good prediction capability. Topomer Search was employed as a tool for virtual screening in lead-like compounds of ZINC database. Finally, 1 R1 group, 7 R2 groups and 6 R3 groups with higher contribution values were employed to alternately substitute the R1, R2 and R3 of the templete compound 23 with highest bioactivity. As a consequence, 33 new molecules with higher activity than that of the model molecule were designed successfully. The results showed that the Topomer Search technology could be effectively apply to screen and design new dipeptidyl boronic acid proteasome inhibitors and has good predictive capability to design new dipeptidyl boronic acid proteasome inhibitors drugs as guidance.

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2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633608003745 ◽

2008 ◽

Vol 07 (02) ◽

pp. 287-301 ◽

Cited By ~ 6

Author(s):

SI YAN LIAO ◽

LI QIAN ◽

JIN CAN CHEN ◽

YONG SHEN ◽

KANG CHENG ZHENG

Keyword(s):

Anticancer Activity ◽

Molecular Design ◽

Predictive Ability ◽

Decisive Role ◽

3D Qsar ◽

Qsar Model ◽

Field Analysis ◽

Qsar Study ◽

Comfa Model ◽

2D Qsar

Two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 23 analogs of 2-Methoxyestradiol with anticancer activity (expressed as p GI50) against MCF-7 human breast cancer cells have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) for 3D. The established 2D-QSAR model in training set shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2= 0.779). The same model was further applied to predict p GI50values of the four compounds in the test set, and the resulting [Formula: see text] being as high as 0.827, further confirms that this 2D-QSAR model has high predictive ability for this kind of compound. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2(0.927) and q2(0.786) obtained from CoMFA model. The results that 2D- and 3D-QSAR analyses accord with each other, suggest that the electrostatic interaction plays a decisive role in determining the anticancer activity of the studied compounds, and that increasing the negative charge of substituent R2and the positive charge of substituents linking to C17as well as decreasing the size of substituent R1are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with anticancer activity.

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3D-QSAR Study on Ring Substituted Imidazoles for Their Antitubercular Activity

Letters in Drug Design & Discovery ◽

10.2174/157018010790225912 ◽

2010 ◽

Vol 7 (2) ◽

pp. 128-132 ◽

Cited By ~ 1

Author(s):

Gyanendra Sharma ◽

Devender Pathak

Keyword(s):

3D Qsar ◽

Antitubercular Activity ◽

Qsar Study

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3D-QSAR and Docking Studies on Pyrimidine Derivatives as CSF-1R Inhibitors

Letters in Drug Design & Discovery ◽

10.2174/1570180816666190329224946 ◽

2020 ◽

Vol 17 (3) ◽

pp. 341-355

Author(s):

Ya-ting Deng ◽

Jun-wei Wang ◽

Han Chu ◽

Juan Wang ◽

Yong Hu ◽

...

Keyword(s):

Poor Prognosis ◽

3D Qsar ◽

Docking Studies ◽

Colony Stimulating Factor ◽

Training Set ◽

Pyrimidine Derivatives ◽

Comfa Model ◽

Inhibitory Activities ◽

The Relationship ◽

Stimulating Factor

Background: Colony Stimulating Factor-1 Receptor (CSF-1R) is associated with malignancy, invasiveness and poor prognosis of tumors, and pyrimidine derivatives are considered as a novel class of CSF-1R inhibitor. Methods: To explore the relationship between the structures of substituted pyrimidine derivatives and their inhibitory activities against CSF-1R, CoMFA and CoMSIA analyses, and molecular docking studies were performed on a dataset of forty-four compounds. Results: We found in CoMFA model including steric and electrostatic fields for the training set, the cross-validated q2 value was 0.617 and the non-cross-validated r2 value was 0.983. While, the crossvalidated q2 value was 0.637 and the non-cross-validated r2 value was 0.984 in CoMSIA Model which include steric, electrostatic and hydrophobic fields. 3D equipotential maps generated from CoMFA and CoMSIA along with the docking binding structures provided enough information about the structural requirements for better activity. Conclusion: The data generated from the present study helped us to predict the activity of new inhibitors and further design some novel and potent CSF-1R inhibitors.

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