scholarly journals A Ligand-Based Approach to Lead Optimization of N, Nʹ-Substituted Diamines for Leishmanicidal Activity

2021 ◽  
Vol 12 (6) ◽  
pp. 7429-7437
Keyword(s):  
Latent Variables ◽  
Leishmania Donovani ◽  
3D Qsar ◽  
Field Analysis ◽  
Leishmanicidal Activity ◽  
Contour Maps ◽  
Lead Compounds ◽  
Robust Model ◽  
High Predictability ◽  
Leave One Out

Several N, N-substituted diamines such as putrescine and N-monoalkylated derivatives have demonstrated potential as lead compounds against Leishmania donovani at submicromolar levels. There is a need to refine available diamines for enhanced leishmanicidal activity. A 3D-QSAR by Comparative molecular field analysis (CoMFA) on a series of tested diamines for their activities against L. donovani was conducted to understand better the mechanism of action and SARs of the compounds. The model was constructed with AM1 energy minimized conformers of the training set compounds (n=20) by the PLS algorithm method, cross-validated by the method of leave-one-out (LOO), and externally validated using the test set compounds (n=5). A robust model with high predictability of untested compounds was obtained for 2PC (latent variables). The coefficients of determinations for PLS regression R2, internal cross-validation, Q2 and external prediction P2 were 0.97 (SDEC=0.095), 0.82 (SDEP=0.102) and 0.73 (SDEP=0.115) respectively with F-value 618.8 for 2PC. The model coefficients graphically translated into contour maps showed regions where steric (62 %) and electrostatic (38 %) properties influence the leishmanicidal activity of the compounds. In addition to the optimum chain length (n=4), a steric effect at position 4 alone or combined with the electrostatic effect at position 3 of the diamine backbone significantly enhanced the leishmanicidal activity. It could further be explored for even higher activity. The model supported the empirical data, which identified N, N'-substituted diamine as the scaffold for leishmanicidal activities and further provided insights for further optimization of the lead compound.

scholarly journals Antiaging, antioxidant flavonoids; synthesis, antimicrobial screening as well as 3D QSAR CoMFA models for the prediction of biological activity

2014 ◽  
Vol 39 (2) ◽  
pp. 191-199 ◽  
Author(s):  
Bilkis Jahan Lumbiny ◽  
Zhang Hui ◽  
M Azizul Islam
Keyword(s):  
Predictive Model ◽  
Biological Activities ◽  
3D Qsar ◽  
Biologically Active ◽  
Field Analysis ◽  
Contour Maps ◽  
Chemical Structures ◽  
Hydroxy Flavone ◽  
Bonding Characteristics ◽  
Better Than

Flavonoids, polyphenolic heteronuclear compounds which are naturally occurring antioxidants are widely used as antiaging substances. Synthesis of new naturally occuring organic compounds with basic skeleton of chalcones, flavones and oxygenated flavones and their antimicrobial activity were reported by this research group for long. Presently comparative molecular field analysis (CoMFA) implemented in Sybyl 7.3 was conducted on a series of substituted flavones. CoMFA is an effective computer implemented 3D QSAR technique deriving a correlation between set of the biologically active molecules and their 3D shape, electrostatic and hydrogen bonding characteristics employing both interactive graphics and statistical techniques. Evaluation of 38 compounds were served to establish the models with grid spacing (2.0 Å). CoMFA produced best predictive model for compound 1C (2 ? Phenyl ? 1,4 ? benzopyrone) and compound 2C (5 ? Fluoro ? 3?? hydroxy flavone ) among all. Model for compound 2C [r2 conv (no-validation) = 0.956, SEE = 0.211, F value = 111.054) is better than that of compound 1C [r2 conv (no-validation) = 0.955, SEE = 0.212, F value = 110.261) but comparing superimposed model 1C being suggested as the best predictive model. 3D contour maps were generated to correlate the biological activities with the chemical structures of the examined compounds and for further design. DOI: http://dx.doi.org/10.3329/jasbs.v39i2.17856 J. Asiat. Soc. Bangladesh, Sci. 39(2): 191-199, December 2013

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 .

scholarly journals 3D-QSAR Study of Indol-2-yl Ethanones Derivatives as Novel Indoleamine 2,3-Dioxygenase (IDO) Inhibitors

2012 ◽  
Vol 9 (4) ◽  
pp. 1753-1759 ◽  
Author(s):  
Kamlendra S. Bhadoriya ◽  
Shailesh V. Jain ◽  
Sanjaykumar B. Bari ◽  
Manish L. Chavhan ◽  
Kuldeep R. Vispute
Keyword(s):  
Nearest Neighbor ◽  
External Validation ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Qsar Study ◽  
Internal Validation ◽  
Contour Maps ◽  
Model Classification ◽  
Ido Inhibitors

3D-QSAR approach usingkNN-MFA was applied to a series of Indol-2-yl ethanones derivatives as novel IDO inhibitors. For the purpose, 22 compounds were used to develop models. To elucidate the structural properties required for IDO inhibitory activity, we report herek-nearest neighbor molecular field analysis (kNN-MFA)-based 3D-QSAR model for Indol-2-yl ethanones derivatives as novel IDO inhibitors. Overall model classification accuracy was 76.27% (q2= 0.7627, representing internal validation) in training set and 79.35% (pred_r2= 0.7935, representing external validation) in test set using sphere exclusion and forward as a method of data selection and variable selection, respectively. Contour maps using this approach showed that hydrophobic and steric effects dominantly determine binding affinities. The information rendered by 3D-QSAR model may lead to a better understanding of structural requirements of IDO inhibitors and can help in the design of novel potent molecules.

scholarly journals 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Study of Thieno[3,2-b]Pyrrole-5- Carboxamide Derivatives as LSD1 Inhibitors

2019 ◽  
Author(s):  
Meilan Huang ◽  
yongtao Xu ◽  
Zihao He ◽  
Min Yang ◽  
Hongyi Liu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Binding Interaction ◽  
Contour Maps

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and become a new target for anticancer drugs. In recent years, the small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamides LSD1 inhibitors using molecular docking, three dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q2=0.783, r2=0.944, r2pred=0.851) and Comparative molecular similarity indices analysis (CoMSIA q2=0.728, r2=0.982, r2pred=0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, the ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.

scholarly journals Designing of the N-ethyl-4-(pyridin-4-yl)benzamide based potent ROCK1 inhibitors using docking, molecular dynamics, and 3D-QSAR

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11951
Author(s):  
Suparna Ghosh ◽  
Seketoulie Keretsu ◽  
Seung Joo Cho
Keyword(s):  
Molecular Dynamics ◽  
Heart Diseases ◽  
3D Qsar ◽  
Structure Activity Relationship ◽  
Field Analysis ◽  
Activity Relationship ◽  
Dimensional Structure ◽  
Valuable Insight ◽  
Contour Maps ◽  
Structure Activity

Rho-associated kinase-1 (ROCK1) has been recognized for its pivotal role in heart diseases, different types of malignancy, and many neurological disorders. Hyperactivity of ROCK phosphorylates the protein kinase-C (PKC), which ultimately induces smooth muscle cell contraction in the vascular system. Inhibition of ROCK1 has been shown to be a promising therapy for patients with cardiovascular disease. In this study, we have conducted molecular modeling techniques such as docking, molecular dynamics (MD), and 3-Dimensional structure-activity relationship (3D-QSAR) on a series of N-ethyl-4-(pyridin-4-yl)benzamide-based compounds. Docking and MD showed critical interactions and binding affinities between ROCK1 and its inhibitors. To establish the structure-activity relationship (SAR) of the compounds, 3D-QSAR techniques such as Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were used. The CoMFA (q2 = 0.774, r2 = 0.965, ONC = 6, and ${r}_{pred}^{2}$ = 0.703) and CoMSIA (q2 = 0.676, r2 = 0.949, ONC = 6, and ${r}_{pred}^{2}$ = 0.548) both models have shown reasonable external predictive activity, and contour maps revealed favorable and unfavorable substitutions for chemical group modifications. Based on the contour maps, we have designed forty new compounds, among which, seven compounds exhibited higher predictive activity (pIC50). Further, we conducted the MD study, ADME/Tox, and SA score prediction using the seven newly designed compounds. The combination of docking, MD, and 3D-QSAR studies helps to understand the coherence modification of existing molecules. Our study may provide valuable insight into the development of more potent ROCK1 inhibitors.

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.

Docking alignment-3D-QSAR of a new class of potent and non-chiral indole-3-carboxamide-based renin inhibitors

2011 ◽  
Vol 76 (12) ◽  
pp. 1447-1469 ◽  
Author(s):  
Jahan B. Ghasemi ◽  
Somayeh Pirhadi
Keyword(s):  
Cross Validation ◽  
3D Qsar ◽  
Initial Orientation ◽  
Test Set ◽  
Docking Analysis ◽  
Renin Inhibitors ◽  
Contour Maps ◽  
New Class ◽  
Qsar Models ◽  
Leave One Out

Using generated conformations from docking analysis by CDOCKER algorithm, some 3D-QSAR models; CoMFA region focusing (CoMFA-RF) and CoMSIA have been created on a series of a new class of potent and non-chiral renin inhibitors. The satisfactory predictions were obtained by CoMFA-RF and CoMSIA based on docking alignment in comparison to CoMFA. Robustness and predictability of the models were further verified by using the test set, cross validation (leave one out and leave ten out), bootstrapping, and progressive scrambling. All-orientation search (AOS) strategy was used to acquire the best orientation and minimize the effect of the initial orientation of aligned compounds. The results of 3D-QSAR models are in agreement with docking results. Moreover, the resulting 3D CoMFA-RF/ CoMSIA contour maps and corresponding models were applied to design new and more active inhibitors.

PREDICTIVE QSAR MODELING OF PYRIDAZINYL DERIVATIVES USING K-NEAREST NEIGHBOR AND PHARMACOPHORE APPROACH

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (07) ◽  
pp. 10-17
Author(s):  
M.C. Sharma ◽  
◽  
D.V. Kohli
Keyword(s):  
Correlation Coefficient ◽  
Nearest Neighbor ◽  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Angiotensin Ii Receptor ◽  
K Nearest Neighbor ◽  
Qsar Modeling ◽  
Leave One Out

This study was carried out elucidate the structural properties required for pyridazinyl derivatives to exhibit angiotensin II receptor activity. The best 2D-QSAR model was selected, having correlation coefficient r2 = 0.8156, cross validated squared correlation coefficient q2 = 0.7348 and predictive ability of the selected model was also confirmed by leave one out cross validation method. Further analysis was carried out using 3D-QSAR method k-nearest neighbor molecular field analysis approach; a leave-one-out crossvalidated correlation coefficient of 0.7188 and a predictivity for the external test set (0.7613) were obtained. By studying the QSAR models, one can select the suitable substituent for active compound with maximum potency.

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.

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