• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.197-201
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• 2011

3D-QSAR techniques including CoMFA have been used a lot for more than two decades now. For now, the perspective of 3D-QSAR has been changed. The realization of gorge activity cliffs and higher chance correlation with many independent variables (IVs) has changed the requirements. Some suggested the benchmarking datasets for 3D-QSAR. However, were they still useful for right reasons? Here, we propose the requirement of any general purpose 3D-QSAR benchmarking datasets for lead optimization, especially for feasibility test of any IVs. Specifically, we summarize the conceptual requirements for an ideal settings for 3D-QSAR especially CoMFA.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.210-215
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• 2011

Holographic quantitative structure-activity relationships (HQSAR) is a useful tool to correlates structures with their biological activities. HQSAR is a two dimensional (2D) QSAR methodology, which generates QSAR equations through 2D fingerprint and correlates it with biological activity. Here, we report a 2D-QSAR model for a series of fifty-one 3,4-dihydroxychalcones derivatives utilizing HQSAR methodology. We developed HQSAR model with 6 optimum numbers of components (ONC), which resulted in cross-validated correlation coefficient ($q^2$) of 0.855 with 0.283 standard error of estimate (SEE). The non-cross-validated correlation coefficient (r2) with 0.966 indicates the model is predictive enough for analysis. Developed HQSAR model was binned in to a hologram length of 257. Atomic contribution map revealed the importance of dihydroxy substitution on phenyl ring.

• Archives of Pharmacal Research
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• v.23 no.4
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• pp.324-328
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• 2000

Three dimensional QSAR studies for antihistamine and antibradykinin effects of new piperazine derivatives were conducted using the comparative molecular field analysis. Electrostatic and steric factors, but not hydrophobic factor, of the synthesized compounds were correlated with the antagonistic effect.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.216-221
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• 2011

c-Jun N-terminal kinase-3 (JNK-3) has been shown to mediate neuronal apoptosis and make the promising therapeutic target for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. In order to better understand the structural and chemical features of JNK-3, comparative molecular field analysis (CoMFA) was performed on a series of 3,5-disubstituted quinolines derivatives. The best predictions were obtained CoMFA model ($q^2$=0.707, $r^2$=0.972) and the statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The resulting contour map from 3D-QSAR models might be helpful to design novel and more potent JNK3 derivatives.

• Reproductive and Developmental Biology
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• v.31 no.1
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• pp.15-20
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• 2007

To search of a new porcine pheromonal odorant for biostimulation control system technologies to offer a potentially useful and practical way to improve reproductive efficiency in livestock species, the two dimensional quantitative structure-activity relationship (QSAR) models between physicochemical parameters as descriptors of 2-cyclohexyloxytetrahydrofurane (A), 2-phenoxytetrahydrofurane (B) analogues and binding affinity constant ($p[Od.]_{50}$) for porcine odorant-binding protein (pOBP) as receptor of pig pheromones were derived and disscused. The statistical quality of the optimized 2D-QSAR model is good ($r^{2}=0.964$) and accounts for 96.4% of the variance in the binding affinity constants. It was found that the binding affinity constants were dependent upon the optimal value, $(SL)_{opt.}=1.418$ of substituent lipole (SL) in molecules. Therefore, the SL constant was very important factor for binding affinity.

• Applied Biological Chemistry
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• v.41 no.6
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• pp.477-482
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• 1998

In order to discover new Leukotriene $D_4$ antagonists, Quantitative Structure-Activity Relationships (QSAR) were applied based on the known data. A series of chalcone derivatives were selected for the training set. A candidate was predicted using QSAR and synthesized, and its biological activity was tested.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• The Korean Journal of Pesticide Science
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• v.14 no.3
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• pp.191-198
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• 2010

To predict and design of new potent insecticidal compounds, the two dimensional quantitative structure-activity relationships (2D-QSARs) and molecular hologram quantitative structure-activity relationships (HQSARs) between the various physicochemical parameters as descripters of N'-phenyl-N-methylformamidine analogues (1-22) and their insecticidal activity against the two spotted spider mite (Tetranychus urticae) were discussed quantitatively. From 2D-QSAR models (1 & 3), the width ($B_2$) of $R_3$-group as sterically factor and optimal total dipole moment (TDM=2.025D) of $R_4$-group were mainly influenced to increase the activity. Therefore, the activities were depend upon the $R_3$- and $R_4$-groups. Particularly, it is predicted that the activity of newly designed potent compound (PI; $EC_{50}$=0.516 ppm) by 2D-QSAR models (3) and HQSAR model F2 was about 34.3 fold higher than that of the commercialized insecticide, Amitraz ($EC_{50}$=17.7 ppm).

• Journal of Integrative Natural Science
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• v.4 no.4
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• pp.266-272
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• 2011

Focal adhesion kinase (FAK) is a potential target for the treatment of primary cancers as well as prevention of tumor metastasis. To understand the structural and chemical features of FAK inhibitors, we report comparative molecular field analysis (CoMFA) for the series of 7H-pyrrolo(2,3-d)pyrimidines. The CoMFA models showed good correlation between the actual and predicted values for training set molecules. Our results indicated the ligand-based alignment has produced better statistical results for CoMFA ($q^2$ = 0.505, $r^2$ = 0.950). Both models were validated using test set compounds, and gave good predictive values of 0.537. The statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The contour map from 3D-QSAR models explains nicely the structure-activity relationships of FAK inhibitors and our results would give proper guidelines to further enhance the activity of novel inhibitors.

• Journal of the Korean Chemical Society
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• v.54 no.1
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• pp.77-87
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• 2010

Hospital-acquired methicillin-resistant Staphylococcus aureus (MRSA) has been an increasing problem worldwide since the initial reports over 40 years ago. To examine new drug leads with potential antibacterial activities, Various N'-[(-3-substituted-4-oxo-1,3-thiazolidin-2-ylidene]-4-hydroxy benzohydrazide (4a-4.i) and N'-[-(3,4-disubstituted)-1,3-thiazolidin-2ylidene)]-4-hydroxybenzohydrazide from (5.a-5.i) to (10.a-10.i) were synthesized using appropriate synthetic route. The entire test compounds (4.a-4.i) and from (5.a-5.i) to (10.a-10.i) were assayed in vitro against s. aureus strain. The minimum inhibitory concentration (MIC) was determined for test compounds and for reference standards. The test compounds showed significant antibacterial activity against the strains used, when tested in vitro. In general, p-hydroxybenzohydrazide ring and substituted thiazoline ring are essential for antimicrobial activity. Among the compounds tested, compounds 6.f, 7.g, 9.f and 10.f, 10 i were found to be most potent. The test compounds were found nontoxic upto the dose level of 2000 ${\mu}g$/mL. The intact compounds were then subjected for 3D-QSAR studies. 3D-QSAR study based on the principal of alignment of pharmacophoric features by Schrodinger PHASE module. The 3D-QSAR study allowed us to confirm the preferential binding mode of p-hydroxybenzohydrazide inside the active site.