• 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.

• 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.

• Journal of Integrative Natural Science
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• v.5 no.1
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• pp.6-12
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• 2012

c-Jun N-terminal kinase-3 (JNK-3) has been identified as a promising target for neuronal apoptosis and has the effective therapeutic for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. Herein, we report the essential structural and chemical parameters for JNK-3 inhibitors utilizing comparative molecular field similarity indices analysis (CoMSIA) using the derivatives of 3,5-disubstituted quinolines. The best predictions were obtained CoMSIA model (q2=0.834, r2=0.987) 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.

• 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).

• 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)

• Journal of Applied Biological Chemistry
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• v.50 no.2
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• pp.52-57
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• 2007

Quantitative structure-activity relationships (QSARs) on the inhibition activities by oleanolic acid analogues (1-19) as a potent inhibitor against protein tyrosine phosphatase-1B were studied quantitatively using 2D-QSAR and HQSAR methodologies. The inhibition activity was dependent on the variations of $R_{4-}$substituent, and as shown in 2D-QSAR model ($r^2=0.928$), it has a tendency to increase as the negative Randic Indice (RI) goes up. The size of the molecular fragments used in HQSAR varied from five to eight. The fragment distinctions had the best statistic value, whose predictability is $q^2=0.785$ and correlation coefficient is $r^2=0.970$, on condition of connections. From the atomic contribution maps, the factor that contributes to the inhibition activities is the $C_{15}{\sim}C_{17}$ bond in the D ring. From the analysis result of these two the models, the structural distinctions and descriptors that contribute to the inhibition activities were obtained.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.40 no.4
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• pp.383-390
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• 2014

The following conclusion was made from the 2D-QSAR model for the tyrosinase inhibitory activity according to the variation of the substituents R1 and R2 in analogues of compound 2-[(2,6-dioxocyclohexyl)methyl]cyclohexane- 1,3-dione (1-23). The best optimized 2D-QSAR model was $Obs.pI_{50}=-0.295({\pm}0.031)TDM$ $-0.120({\pm}0.014)DMZ+0.135({\pm}0.050)DMX.R_2+6.382({\pm}0.17)$, and the correlation $r^2=0.905$) of which was greater than its predictability ($q^2=0.843$). The magnitude of the effect of tyrosinase inhibitory activities was in order of TDM > $DMX.R_2{\geq}DMZ$, and it tended to increase as the hydrophobicity of substrate molecule (ClogP > 0) as well as the steric favor of substituent $R_1$ increased. The analysis of the model implies that inhibitory activity of substrate molecule will increase as $DMX.R_2$ (Dipole moment X component of $R_2$-substituent) increases, while TDM (Total Dipole Moment) and DMZ(Dipole Moment of Z-Component) decrease. As such, it is deemed feasible to conclude, that in order to increase the inhibitory effect, it would be rather desirable to replace the polar groups within the molecules with non-polar functional groups.

• 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.

• Journal of the Korean Chemical Society
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• v.57 no.6
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• pp.744-754
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• 2013

Quantitative structure-activity relationship (QSAR) analysis for recently synthesized imidazole-(benz)azole and imidazole - piperazine derivatives was studied for their anticancer activities against breast (MCF-7) cell lines. The statistically significant 2D-QSAR models ($r^2=0.8901$; $q^2=0.8130$; F test = 36.4635; $r^2$ se = 0.1696; $q^2$ se = 0.12212; pred_$r^2=0.4229$; pred_$r^2$ se = 0.4606 and $r^2=0.8763$; $q^2=0.7617$; F test = 31.8737; $r^2$ se = 0.1951; $q^2$ se = 0.2708; pred_$r^2=0.4386$; pred_$r^2$ se = 0.3950) were developed using molecular design suite (VLifeMDS 4.2). The study was performed with 18 compounds (data set) using random selection and manual selection methods used for the division of the data set into training and test set. Multiple linear regression (MLR) methodology with stepwise (SW) forward-backward variable selection method was used for building the QSAR models. The results of the 2D-QSAR models were further compared with 3D-QSAR models generated by kNN-MFA, (k-Nearest Neighbor Molecular Field Analysis) investigating the substitutional requirements for the favorable anticancer activity. The results derived may be useful in further designing novel imidazole-(benz)azole and imidazole-piperazine derivatives against breast (MCF-7) cell lines prior to synthesis.

• 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.