• The Korean Journal of Pesticide Science
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• v.11 no.2
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• pp.72-81
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• 2007

Quantitative structure-activity relationships (QSARs) on the pre-emergency herbicidal activity and reactivity of a series of new O,O-dialkyl-1-phenoxyacetoxy-1-methylphosphonates (S) analogues against seed of cucumber (Cucumus Sativa) were discussed quantitatively using 2D-QSAR and HQSAR methods. The statistical values of HQSAR model were better than that of 2D-QSAR model. From the frontier molecular orbital (FMO) interaction between substrate molecule (S) and $BH^+$ ion (I) in PDH enzyme, the electrophilic reaction was superior in reactivity. From the effect of substituents, $R_2$-groups in substrate molecule (S) contributed to electrophilic reaction with carbonyl oxygen atom while X, Y-groups contributed to nucleophilic reaction with carbonyl carbon atom. And the influence of X,Y-groups was more effective than that of $R_2$-groups. As a results of 2D-QSAR model (I & II) and atomic contribution maps with HQSAR model, the more length of X, Y-groups is longer, the more herbicidal activity tends to increased. And also, the optimal ${\epsilon}LUMO$ energy, $({\epsilon}LUMO)_{opt.}$=-0.479 (e.v.) of substrate molecule is important factor in determining the herbicidal activity. It is predicted that the herbicidal activity proceeds through a nucleophilic reaction. From the analytical results of 2D-QSAR and HQSAR model, it is suggested that the structural distinctions and descriptors that contribute to herbicidal activities will be able to applied new herbicide design.

• The Korean Journal of Pesticide Science
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• v.12 no.1
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• pp.9-17
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• 2008

QSARs (Quantitative structure-activity relationships) between a series of new cyclohexanedione derivatives (5-benzofuryl-2-[1-(alkoxyimino)-alkyl]-3-hydroxycyclohex-2-en-1-ones) and their herbicidal activity against Rice plant (Oryza sativa L.) and Barnyard grass (Echinochloa crus-galli.) were discussed quantitatively using 2D-QSAR and holographic (H) QSAR methods. Generally, the HQSAR models have better predictability and fitness than the 2D-QSAR models. The herbicidal activities against Barnyard grass with 2D-QSAR II model were dependent upon Balaban indice (BI) of molecule and hydrophobicity of $R_1$ and $R_3$ group. And also, the $R_3=ethyl$ group, according to the information of the optimized HQSAR IV model, was more contribute to the herbicidal activities against Rice plant, while the 5-(cyclohex-3-enyl)-2,3-dihydrobenzofuran ring part was not contribute to the herbicidal activities against two plants.

• 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.

• 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.

• 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.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.

• 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.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.

• 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.

• 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.