• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.210-218
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• 2004

Poly(ADP-ribose)polymerase-1 (PARP-1) is a nuclear enzyme involved in various physical functions related to genomic repair, and PARP inhibitors have therapeutic application in a variety of neurological diseases. Docking and the QSAR (quantitative structure-activity relationships) studies for 52 PARP-1 inhibitors were conducted using FlexX algorithm, comparative molecular field analysis (CoMFA), and hologram quantitative structure-activity relationship analysis (HQSAR). The resultant FlexX model showed a reasonable correlation (r$^{2}$ = 0.701) between predicted activity and observed activity. Partial least squares analysis produced statistically significant models with q$^{2}$ values of 0.795 (SDEP=0.690, r$^{2}$=0.940, s=0.367) and 0.796 (SDEP=0.678, r$^{2}$ = 0.919, s=0.427) for CoMFA and HQSAR, respectively. The models for the entire inhibitor set were validated by prediction test and scrambling in both QSAR methods. In this work, combination of docking, CoMFA with 3D descriptors and HQSAR based on molecular fragments provided an improved understanding in the interaction between the inhibitors and the PARP. This can be utilized for virtual screening to design novel PARP-1 inhibitors.

• The Journal of Internal Korean Medicine
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• v.27 no.4
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• pp.888-894
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• 2006

Objectives : For the purpose of developing new anti-HIV agents from natural sources, the extracts of Ricinus communis were tested for their inhibitory effects on essential enzymes reverse transcriptase (RT), protease and alpha-glucosidase. Inhibition activity of major compounds of Ricinus communis were predicted from quantitative structure activity relationships (QSAR) in silico. Methods and Results : In the anti-HIV-1 RT using enzyme-linked oligonucleotide sorbent assay (ELOSA) method, water and methanol extracts (100ug/ml) of Ricinus communis showed strong activity of 94.2% and 82.7%, respectively. In the HIV-1 protease and alpha-glucosidase inhibition assay, neither water nor methanol extracts of Ricinus communis inhibited the activity of the enzyme to cleave any substrates as oligopeptides and oligosaccharides. Conclusions : We found that for these samples it is possible that the inhibition of the RT in vitro is due to the secondary metabolites of Ricinus communis such as ricinine and quercetin. It would beof great interest to identify the compounds which are responsible for this inhibition, since all therapeutically useful agents up to date are RT inhibitors.

• YAKHAK HOEJI
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• v.55 no.2
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• pp.98-105
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• 2011

To search the minimum structural requirement of tricyclic isoxazole analogues (1~30) as new class potent antidepressant, thee-dimensional quanti- tative-structure relationship (3D-QSAR) models between substituents ($R_1{\sim}R_5$) of tricyclic isoxazoles and their antidepressant activity against medetomidine-induced loss of righting were performed and discussed quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indies analysis (CoMSIA) methods. The correlativity and predictability ($r^2$=0.484 and $q^2$=0.947) of CoMSIA-2 model were higher than those of the rest models. The inhibitory activity against medetomidine-induced loss of righting was dependent on electrostatic field (43.4%), hydrophobic field (35.3%), and steric field (21.2%) of tricyclic isoxazoles. From the CoMSIA-2 contour maps, it is predicted that the antidepressant activity of potent antidepressants against medetomidine-induced loss of righting will be able to increase by the substituents ($R_1{\sim}R_5$) which were in accord with CoMSIA field.

• YAKHAK HOEJI
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• v.55 no.2
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• pp.91-97
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• 2011

To search a new anti-depressant agents against para-chloroamphetamine-induced excitation, three dimensional quantitative-structure relationships (3D-QSAR) models between structure of 3a,4-dihydro-3H-[1]-benzopyronao[4,3]isoxazoles (1-30) and thieir inhibitory activity against para-chloroamphetamine-induced excitation were performed and discussed quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. From these basis on the findings, the optimized CoMSIA-2F model ($q^2$=0.793 and $r^2$=0.952) showed the best statistical results. And also, it is found that the para-chloroamphetamine inhibitory activity from the optimized CoMSIA-2F model was dependent on steric field (35.2%) and electrostatic field (64.8%) of tricyclic isoxazoles. Particularly, it is predicted that the inhibitory activity against para-chloroamphetamine-induced excitation will be able to increase by the designed compounds from the CoMSIA-2F model.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.91-98
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• 2011

Quantitative structure-activity relationships (QSAR) have been applied for two decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the QSAR is that the structural difference is responsible for the variations in biological activities of the compounds. In this work, we developed 3D-QSAR model for a series of 5-Lipoxygenase inhibitors, utilizing comparative molecular field analysis (CoMFA) and Topomer CoMFA methodologies. Our developed models addressed superiority of Topomer CoMFA over CoMFA. The CoMFA model was obtained with $q^2$=0.593, $r^2$=0.939, $Q^2$=0.334 with 6 optimum number of components (ONC). Higher statistical results were obtained with the Topomer CoMFA model ($q^2$=0.819, $r^2$=0.947, ONC=5). Further robustness of developed models was checked with the ANOVA test and it shows F=113 for CoMFA and F=162.4 for Topomer CoMFA model. Contour map analysis indicated that the more requirement of electrostatic parameter for improved potency.

• Bulletin of the Korean Chemical Society
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• v.26 no.6
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• pp.952-958
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• 2005

Angiotensin-converting enzyme (ACE) is known to be primarily responsible for hypertension. Threedimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of 28 ACE inhibitors. The availability of ACE crystal structure (1UZF) provided the plausible biological orientation of inhibitors to ACE active site (C-domain). Alignment for CoMFA obtained by docking ligands to 1UZF protein using FlexX program showed better statistical model as compared to superposition of corresponding atoms. The statistical parameters indicate reasonable models for both CoMFA ($q^2$ = 0.530, $r^2$ = 0.998) and CoMSIA ($q^2$ = 0.518, $r^2$ = 0.990). The 3D-QSAR analyses provide valuable information for the design of ACE inhibitors with potent activity towards C-domain of ACE. The group substitutions involving the phenyl ring and carbon chain at the propionyl and sulfonyl moieties of captopril are essential for better activity against ACE.

• Bulletin of the Korean Chemical Society
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• v.30 no.11
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• pp.2739-2748
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• 2009

Hologram and three dimensional quantitative structure activity relationship (3D QSAR) studies for a series of anilinobipyridine JNK3 inhibitors were performed using various alignment-based comparative molecular field analysis (COMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro JNK3 inhibitory activity exhibited a strong correlation with steric and electrostatic factors of the molecules. Using four different types of alignments, the best model was selected based on the statistical significance of CoMFA ($q_2\;=\;0.728,\;r_2\;=\;0.865$), CoMSIA ($q_2\;=\;0.706,\;r_2\;=\;0.960$) and Hologram QSAR (HQSAR: $q_2\;=\;0.838,\;r_2\;=\;0.935$). The graphical analysis of produced CoMFA and CoMSIA contour maps in the active site indicated that steric and electrostatic interactions with key residues are crucial for potency and selectivity of JNK3 inhibitors. The HQSAR analysis showed a similar qualitative conclusion. We believe these findings could be utilized for further development of more potent and selective JNK3 inhibitors.

• Journal of Environmental Health Sciences
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• v.40 no.2
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• pp.105-113
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• 2014

Objectives: It is necessary to apply quantitative structure activity relationship (QSAR) for the various chemicals with insufficient toxicity data that are used in the workplace, based on the precautionary principle. This study aims to find application plan of QSAR software tool for predicting health hazards such as genetic toxicity, and carcinogenicity for some chemicals used in the electronics industries. Methods: Toxicity prediction of 21 chemicals such as 5-aminotetrazole, ethyl lactate, digallium trioxide, etc. used in electronics industries was assessed by Toxicity Prediction by Komputer Assisted Technology (TOPKAT). In order to identify the suitability and reliability of carcinogenicity prediction, 25 chemicals such as 4-aminobiphenyl, ethylene oxide, etc. which are classified as Group 1 carcinogens by the International Agency for Research on Cancer (IARC) were selected. Results: Among 21 chemicals, we obtained prediction results for 5 carcinogens, 8 non-carcinogens and 8 unpredictability chemicals. On the other hand, the carcinogenic potential of 5 carcinogens was found to be low by relevant research testing data and Oncologic TM tool. Seven of the 25 carcinogens (IARC Group 1) were wrongly predicted as non-carcinogens (false negative rate: 36.8%). We confirmed that the prediction error could be improved by combining genetic toxicity information such as mutagenicity. Conclusions: Some compounds, including inorganic chemicals and polymers, were still limited for applying toxicity prediction program. Carcinogenicity prediction may be further improved by conducting cross-validation of various toxicity prediction programs, or application of the theoretical molecular descriptors.

• BMB Reports
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• v.31 no.5
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• pp.459-467
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• 1998

A new set of functional group interaction energy descriptors relevant to the ACE (Angiotensin-Converting Enzyme) inhibitory peptide, QSAR (Quantitative Structure Activity Relationships), is presented. The functional group interaction energies approximate the charged interactions and distances between functional groups in molecules. The effective energies of the computationally derived geometries are useful parameters for deriving 3D-QSAR models, especially in the absence of experimentally known active site conformation. ACE is a regulatory zinc protease in the renin-angiotensin system. Therapeutic inhibition of this enzyme has proven to be a very effective treatment for the management of hypertension. The non bond interaction energy values among functional groups of six-feature of ACE inhibitory peptides were used as descriptor terms and analyzed for multivariate correlation with ACE inhibition activity. The functional group interaction energy descriptors used in the regression analysis were obtained by a series of inhibitor structures derived from molecular mechanics and semi-empirical calculations. The descriptors calculated using electrostatic and steric fields from the precisely defined functional group were sufficient to explain the biological activity of inhibitor. Application of the descriptors to the inhibition of ACE indicates that the derived QSAR has good predicting ability and provides insight into the mechanism of enzyme inhibition. The method, functional group interaction energy analysis, is expected to be applicable to predict enzyme inhibitory activity of the rationally designed inhibitors.

• Bulletin of the Korean Chemical Society
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• v.35 no.12
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• pp.3637-3641
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• 2014

In this study, we analyzed the toxicity of mixtures of dimethylformamide (DMF) and methyl ethyl ketone (MEK) or DMF and toluene (TOL) and predicted their toxicity using quantitative structure-activity relationships (QSAR). A QSAR model for single substances and mixtures was analyzed using multiple linear regression (MLR) by taking into account the statistical parameters between the observed and predicted $EC_{50}$. After preprocessing, the best subsets of descriptors in the learning methods were determined using a 5-fold cross-validation method. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP), flash point (FP), low explosion limit (LEL), and octanol/water partition coefficient (Pow) were observed between the single substances and the mixtures. The $EC_{50}$ of the mixture of DMF and TOL was significantly lower than that of DMF. The mixture toxicity was directly related to the mixing ratio of TOL and MEK (MLR $EC_{50}$ equation = $1.76997-1.12249{\times}TOL+1.21045{\times}MEK$), as well as to SG, VP, and LEL (MLR equation $EC_{50}=15.44388-19.84549{\times}SG+0.05091{\times}VP+1.85846{\times}LEL$). These results show that QSAR-based models can be used to quantitatively predict the toxicity of mixtures used in manufacturing industries.