• The Korean Journal of Pesticide Science
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• v.7 no.2
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• pp.108-116
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• 2003

A series of new quinclorac family, herbicidal 3-phenyl-5-(3,7-dichloro-8-quinolinyl)-1,2,4-oxadiazole derivatives as substrate were synthesized and their growth inhibition activity $(pI_{50})$ against root and shoot of rice plant (Oryza sativa L.) and barnyard grass (Echinochloa crus-galli) were determined. And then comparative molecular field analysis (CoMFA) and molecular holographic quantitative structure- activity relationship (HQSAR) were compared in terms of their potential for predictiability. The statistical results were suggested that HQSAR based model had better predictability than CoMFA model. The selective factors to remove barnyard grass take electron withdrawing groups which can be created positive charge and steric bulky on the phenyl ring. Results revealed that the unknown 2,6-dichloro-substituent, U5 and 2,4,6-trichloro-substituent, U6(${\Delta}pI_{50}$=CoMFA: 1.18 & HQSAR: 1.82) were predicted as compound with higher activity and selectivity.

• The Korean Journal of Pesticide Science
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• v.9 no.2
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• pp.146-152
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• 2005

The fungicidal activities against resistance phytophthora blight (RPC; 95CC7303) and sensitive phytophthora blight (Phytopthora capsici) (SPC; 95CC7105) by new 2-alkoxyphenyl-3-phenylthioisoindoline-1-one derivatives (A & B) were studied using molecular holographic quantitative structure activity relationships (HQSAR) methodology. Based on the results, the statistical results of the two best HQSAR models, RI-B for RPC and SII-A for SPC exhibited the best predictability and fitness for the fungicidal activities based on the cross-validated value ($q^2=0.806{\sim}0.865$) and non cross-validated value ($r^2_{ncv.}=0.921{\sim}0.952$, respectively. The quality of the model for SPC was slightly than that of RPC. From the based graphical analyses of atomic contribution maps, it was confirmed that the novel selective character for fungicidal activities against RPC depends upon the 2-fluoro-4-chloro-5-alkoxyanilino group.

• Molecular & Cellular Toxicology
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• v.2 no.3
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• pp.216-221
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• 2006

The cholinesterase-inhibiting organophosphorous (OP) compounds known as nerve agents are highly toxic. The principal toxic mechanism of OP compounds is the inhibition of acethylcholine esterase (AChE) by phosphorylation of its catalytic site. The reversible competitive inhibition of AChE may prevent the subsequent OP intoxication. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) was performed to investigate the relationship between the 29 compounds with structural diversity and their bioactivities against AChE. In particular, predictive models were constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The results indicate reasonable model for CoMFA ($q^{2}=0.453,\;r^{2}=0.697$) and CoMSIA ($q^{2}=0.518,\;r^{2}=0.696$). The presence of steric and hydophobic group at naphtyl moiety of the model may lead to the design of improved competitive inhibitors for organophosphorous intoxication.

• Journal of Integrative Natural Science
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• v.8 no.3
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• pp.214-220
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• 2015

Cancer has emerged as one of the leading cause of deaths worldwide. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of quinazoline-based anticancer agents. Purpose of the study is to understand the structural basis for their inhibitory activity. Comparative molecular field analysis (CoMFA) technique was employed to develop 3D-QSAR model. Ligand-based alignment scheme was used to generate a reliable CoMFA model. The model produced statistically significant results with a cross-validated correlation coefficient ($q^2$) of 0.589 and a non-cross-validated correlation coefficient ($r^2$) of 0.928. Model was further validated by bootstrapping and progressive scrambling analysis. This study could assist in the design of novel and more potent anticancer agents.

• Journal of Integrative Natural Science
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• v.9 no.1
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• pp.1-9
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• 2016

Leucine rich repeat kinase 2 (LRRK2) is a highly promising target for Parkinson's disease (PD) that affects millions of people worldwide. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of pyrrolopyrimidine-based selective LRRK2 kinase inhibitors. This study was performed to rationalize the structural requirements responsible for the inhibitory activity of these compounds. A reliable 3D-QSAR model was developed using comparative molecular field analysis (CoMFA) technique. The model produced statistically acceptable results with a cross-validated correlation coefficient ($q^2$) of 0.539 and a non-cross-validated correlation coefficient ($r^2$) of 0.871. Robustness of the model was further evaluated by bootstrapping and progressive scrambling analysis. This work could assist in designing more potent LRRK2 inhibitors.

• Genomics & Informatics
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• v.5 no.1
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• pp.24-29
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• 2007

Computational virtual screening has become an essential platform of drug discovery for the efficient identification of active candidates. Moleculardocking, a key technology of receptor-centric virtual screening, is commonly used to predict the binding affinities of chemical compounds on target receptors. Despite the advancement and extensive application of these methods, substantial improvement is still required to increase their accuracy and time-efficiency. Here, we evaluate several advanced structure-based virtual screening approaches for elucidating the rank-order activity of chemical libraries, and the quantitative structureactivity relationship (QSAR). Our results show that the ensemble-average free energy estimation, including implicit solvation energy terms, significantly improves the hit enrichment of the virtual screening. We also demonstrate that the assignment of quantum mechanical-polarized (QM-polarized) partial charges to docked ligands contributes to the reproduction of the crystal pose of ligands in the docking and scoring procedure.

• Journal of IKEEE
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• v.21 no.3
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• pp.309-319
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• 2017

The research investigates the inhibition of fatty acid biosynthesis of the human Acetyl-CoA Carboxylase enzyme by the aromatic-structure inhibitors (also known as ligands) containing variables of substituents, contributing an important role in the treatment of fatty-acid metabolic syndrome expressed by the group of cardiovascular risk factors increasing the incidence of coronary heart disease and type-2 diabetes. The effective interoperability between ligand and enzyme is characterized by a 50% concentration of enzyme inhibitor ($IC_{50}$) which was determined by experiment, and the factor of geometry structure of the ligands which are modeled by quantum mechanical methods using HyperChem 8.0.10 and Gaussian 09W softwares, combining with the calculation of quantum chemical and chemico-physical structural parameters using HyperChem 8.0.10 and Padel Descriptor 2.21 softwares. The result data are processed with the combination of classical statistical methods and modern bioinformatics methods using the statistical softwares of Department of Pharmaceutical Technology - Jadavpur University - India and R v3.3.1 software in order to accomplish a model of the quantitative structure - activity relationship between aromatic-structure ligands inhibiting fatty acid biosynthesis of the human Acetyl-CoA Carboxylase.

• Bulletin of the Korean Chemical Society
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• v.34 no.6
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• pp.1823-1828
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• 2013

Tetraoxanes (1,2,4,5-tetraoxanes) have been reported to exhibit potent antimalarial activity. In the present study, the three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of tetraoxanes derivatives using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The best predictive CoMFA model with atom fit alignment resulted in cross-validated coefficient ($q^2$) value of 0.719, non-cross-validated coefficient ($r^2$) value of 0.855 with standard error of estimate (SEE) 0.335. Similarly, the best predictive CoMSIA model was derived with $q^2$ of 0.739, $r^2$ of 0.847 and SEE of 0.344. The generated models were externally validated using test sets. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel tetraoxanes having improved antimalarial activity.

• Bulletin of the Korean Chemical Society
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• v.27 no.10
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• pp.1531-1536
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• 2006

Microsomal prostaglandin $E_2$ synthase (mPGES-1) is an enzyme that is associated with inflammation, pain, fever and cancer. Hologram quantitative structure activity relationship (HQSAR) was conducted on the series of MK-886 compounds acting as mPGES-1 inhibitors. A training set with 24 compounds was used to establish the HQSAR model. The best model was chosen based on the cross-validated correlation coefficient ($q^2$=0.884) and the correlation coefficient($r^2$=0.976). The model was utilized to predict the activity of the eight-test set of compounds giving the predictive $r^2$ value of 0.845. The descriptors of the model are based on fragment distinction (atoms, bond and connectivity) and fragment size (2-5 atoms). The atomic contribution maps generated from HQSAR were useful in identifying the important structural features responsible for the inhibitory activity of MK-886 inhibitors. Based on the generated model, the presence of hydrophobic biphenyl group seems to enhance inhibition of mPGES-1 that is in agreement with the previous experiments. In addition, it seems important for a halogen to be substituted to the biphenyl ring and for an acyl group to be attached to the indole moiety for enhanced activity.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.45-49
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• 2014

The (c-Jun N-terminal kinase 3) JNK3 is a potential therapeutic target for various neurological disorders. Here, a three dimensional quantitative structure-activity relationship (3D-QSAR) study on phenoxypyridine as JNK3 inhibitors was performed to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The comparative molecular field analysis (CoMFA) using different partial atomic charges, was employed to understand the structural factors affecting JNK3 inhibitory potency. The Gasteiger-Marsili yielded a CoMFA model with cross-validated correlation coefficient ($q^2$) of 0.54 and non-cross-validated correlation coefficient ($r^2$) of 0.93 with five components. Furthermore, contour maps suggested that bulky substitution with oxygen atom in $R^3$ position could enhance the activity considerably. The work suggests that further chemical modifications of the compounds could lead to enhanced activity and could assist in the design of novel JNK3 inhibitors.