• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.647-655
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• 2008

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

• 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.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.249.2-250
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• 2003

3D-QSAR analyses by CoMFA and CoMSIA were conducted on a series of thiazole and triazole analogues with respect to their antifungal activities against Microsporum gypseum. A total of twenty analogues were used for the derivation of the 3D-QSAR models (training set). Thesuperposition of the compounds was performed by applying the FlexS with shape-based screening method. (omitted)

• Bulletin of the Korean Chemical Society
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• v.25 no.10
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• pp.1513-1520
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• 2004

We investigate a series of synthesized ${\beta}$-methoxyacrylate analogues for their 3D QSAR & HQSAR against Magnaporthe grisea (Rice Blast Disease). We perform the three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies, using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) procedure. In addition, we carry out a two-dimensional Quantitative Structure-Activity Relationship (2D-QSAR) study, using the Hologram QSAR (HQSAR). We perform these studies, using 53 compounds as a training set and 10 compounds as a test set. The predictive QSAR models have conventional $r^2$ values of 0.955 at CoMFA, 0.917 at CoMSIA, and 0.910 at HQSAR respectively; similarly, we obtain cross-validated coefficient $q^2$ values of 0.822 at CoMFA, 0.763 at CoMSIA, and 0.816 at HQSAR, respectively. From these studies, the CoMFA model performs better than the CoMSIA model.

• 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.

• 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).

• Applied Biological Chemistry
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• v.50 no.3
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• pp.147-153
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• 2007

To search for a molecular design of a new breast cancerous inhibitory active compound, 2D-QSAR and HQSAR between the substituents of flavopiridol analogues as substrates and their breast cancerous inhibitory activities against MCF-7 cell were analyzed and discussed quantitatively. It was found that the dispersion with molecule and steric hindrance with substituents will have a tremendous impact on the inhibitory activities from the 2D-QSAR model (1). Also, MR constant is better than that of MS constant as animportant factor. The inhibitory activities from 2D-QSAR model (2) were dependent upon the optimum MR constant (MR = 126 $Cm^3/mol$). Optimized HQSAR model (V) exhibited the best predictability of the inhibitory activities based on the cross-validated $r^2_{cv}$($q^2$= 0.583) and non-cross-validated conventional coefficient ($r^2_{ncv}$= 0.982). From the contribution maps, the inhibitory activity by the imino group on $C_9$ atom was higher than that of the hydroxyl group of $C_8$ atom on the A ring in molecule. Therefore, we can confirm that the dispersion by substituents in molecule is the most important factor in inhibitory activities against MCF-7 cell.

• Reproductive and Developmental Biology
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• v.33 no.3
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• pp.119-123
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• 2009

The two dimensional quantitative structure-activity relationships (2D-QSARs) models concerning the binding affinity constants ($p[Od.]_{50}$) between 2-cyclohexyltetrahydropyrane and 2-cyclohexyltetrahydrofurane analogues as substrates, and bovine odorant binding protein (bOBP) as receptor were derived by multiple regression analyses method and discussed. The statistical quality of the optimized 2D-QSAR model (5) was good (r=0.907). From the model, the binding affinity constants ($p[Od.]_{50}$) were dependent upon the optimal value ($(TL)_{opt.}$=2.737) of total lipole (TL) of substrate molecules. Based on these findings, the high active compounds predicted by optimized 2D-QSAR model (5) were 2-(dimethylcyclohexyl)tetrahydropyrane molecule and their isomer molecules. The binding affinity constants regarding bOBP of the tetrahydrofuryl-2-yl family compounds were dependent upon the hydrophobicity (logP) of whole substrate molecules. In any case of porcine odorant-binding proteins (pOBP), the constants were dependent upon the hydrophobicity (${\pi}x={\log}P_X-{\log}P_H$) of substituents (R) in substrate molecules. Also, from the optimal values of hydrophobic constant, the hydrophobicity for bOBP influenced ca. twice time bigger (bOBP>pOBP) than that for pOBP.

• 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.

• Bulletin of the Korean Chemical Society
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• v.30 no.9
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• pp.2117-2120
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• 2009