• Journal of applied mathematics & informatics
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• v.39 no.3_4
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• pp.339-358
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• 2021

Zagreb indices and their modified versions of a molecular graph are important descriptors which can be used to characterize the structural properties of organic molecules from different aspects. In this work, we investigate some properties of the modified second multiplicative Zagreb index of graphs with given connectivity. In particular, we obtain the maximum values of the modified second multiplicative Zagreb index with fixed number of cut edges, or cut vertices, or edge connectivity, or vertex connectivity of graphs. Furthermore, we characterize the corresponding extremal graphs.

• Bulletin of the Korean Chemical Society
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• v.30 no.1
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• pp.67-76
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• 2009

The development of a variety of methods like AM1, PM3, PM5 and DFT now allows the calculation of atomic and molecular properties with high precision as well as the treatment of large molecules with predictive power. In this paper, these methods have been used to calculate a number of quantum chemical descriptors (like Klopman atomic softness in terms of $E_n^{\ddag}\;and\;E_m^{\ddag}$, chemical hardness, global softness, electronegativity, chemical potential, electrophilicity index, heat of formation, total energy etc.) for 75 alkanes to predict their boiling point values. The 3D modeling, geometry optimization and semiempirical & DFT calculations of all the alkanes have been made with the help of CAChe software. The calculated quantum chemical descriptors have been correlated with observed boiling point by using multiple linear regression (MLR) analysis. The predicted values of boiling point are very close to the observed values. The values of correlation coefficient ($r^2$) and cross validation coefficient ($r_{cv}^2$) also indicates the generated QSPR models are valuable and the comparison of all the methods indicate that the DFT method is most reliable while the addition of Klopman atomic softness $E_n^{\ddag}$ in DFT method improves the result and provides best correlation.

• Bulletin of the Korean Chemical Society
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• v.34 no.3
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• pp.837-850
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• 2013

Bovine viral diarrhea virus (BVDV), a major pathogen of cattle, is a well-characterized pestivirus which has been used as a good model virus for HCV. The RNA-dependent RNA polymerase (RdRp) plays a key role in the RNA replication process, thus it has been targeted for antivirus drugs. We employed two-dimensional quantitative structure-activity relationship (2D-QSAR) and molecular field analysis (MFA) to identify the molecular substructure requirements, and the particular characteristics resulted in increased inhibitory activity for the known series of compounds to act as effective BVDV inhibitors. The 2D-QSAR study provided the rationale concept for changes in the structure to have more potent analogs focused on the class of arylazoenamines, benzimidazoles, and acridine derivatives with an optimal subset of descriptors, which have significantly contributed to overall anti-BVDV activity. MFA represented the molecular patterns responsible for the actions of antiviral compound at their receptors. We conclude that the polarity and the polarizability of a molecule play a main role in the inhibitory activity of BVDV inhibitors in the QSAR modeling.

• Advances in environmental research
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• v.1 no.4
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• pp.257-276
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• 2012

QSAR models for chlorinated alkenes were developed between $E_{HOMO}$ and their chlorine and carbon content. The aim is to provide valid QSAR model which is statistically validated for $E_{LUMO}$ prediction. Different molecular descriptors, $N_{Cl}$, $N_C$ and $E_{HOMO}$ have been used to take into account relevant information provided by molecular features and physicochemical properties. The best model were selected using Partial Least Square (PLS) and Multiple Linear Regression (MLR) led to models with satisfactory predictive ability for a data set of 15 chlorinated alkene compounds.

• 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.

• Bulletin of the Korean Chemical Society
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• v.35 no.9
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• pp.2679-2683
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• 2014

Mechanisms underlying the impacts of interactions between carbon nanoparticles (CNPs) and ionic liquids (ILs) on the physicochemical behavior of CNPs need to be more full worked out. This manuscript describes a theoretical investigation at multiple levels on the interactions of fullerene $C_{60}$ with 21 imidazolium-based ILs of varying alkyl side chain lengths and anionic types and their impacts on $C_{60}$ dispersion behavior. Results show that ${\pi}$-cation interaction contributed to mechanism of the $C_{60}$-IL interaction more than ${\pi}$-anion interaction. The calculated interaction energy ($E_{INT}$) indicates that $C_{60}$ can form stable complex with each IL molecule. Moreover, the direction of charge transfer occurred from IL to $C_{60}$ during the $C_{60}$-IL interaction. Quantitative models were developed to evaluate the self-diffusion coefficient of $C_{60}$ ($D_{fullerene}$) in bulk ILs. Three interpretative molecular descriptors (heat of formation, $E_{INT}$, and charge) that describe the $C_{60}$-IL interactions and the alkyl side chain length were found to be determinants affecting $D_{fullerene}$.

• Bulletin of the Korean Chemical Society
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• v.34 no.9
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• pp.2680-2684
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• 2013

Protein kinases (PKs) are an important source of drug targets, especially in oncology. With 500 or more kinases in the human genome and only few kinase inhibitors approved, kinase inhibitor discovery is becoming more and more valuable. Because the discovery of kinase inhibitors with an increased selectivity is an important therapeutic concept, many researchers have been trying to address this issue with various methodologies. Although many attempts to predict the activity and selectivity of kinase inhibitors have been made, the issue of selectivity has not yet been resolved. Here, we studied kinase selectivity by generating predictive models and analyzing their descriptors by using kinase-profiling data. The 5-fold cross-validation accuracies for the 51 models were between 72.4% and 93.7% and the ROC values for all the 51 models were over 0.7. The phylogenetic tree based on the descriptor distance is quite different from that generated on the basis of sequence alignment.

• Interdisciplinary Bio Central
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• v.4 no.1
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• pp.2.1-2.7
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• 2012

Introduction: Histone deacetylases (HDAC) are a class of enzymes that remove acetyl groups from ${\varepsilon}$-N-acetyl lysine amino acids of histone proteins. Their action is opposite to that of histone acetyltransferase that adds acetyl groups to these lysines. Only few HDAC inhibitors are approved and used as anti-cancer therapeutics. Thus, discovery of new and potential HDAC inhibitors are necessary in the effective treatment of cancer. Materials and Methods: This study proposed a method using support vector machine (SVM) to classify HDAC8 inhibitors and non-inhibitors in early-phase virtual compound filtering and screening. The 100 experimentally known HDAC8 inhibitors including 52 inhibitors and 48 non-inhibitors were used in this study. A set of molecular descriptors was calculated for all compounds in the dataset using ADRIANA. Code of Molecular Networks. Different kernel functions available from SVM Tools of free support vector machine software and training and test sets of varying size were used in model generation and validation. Results and Conclusion: The best model obtained using kernel functions has shown 75% of accuracy on test set prediction. The other models have also displayed good prediction over the test set compounds. The results of this study can be used as simple and effective filters in the drug discovery process.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4397-4402
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• 2011

The NF-${\kappa}B$ system of transcription factors plays a crucial role in inflammatory diseases, making it an important drug target. We combined quantitative structure activity relationships for predicting the activity of new compounds and quantitative dynamic models for the NF-${\kappa}B$ network with intracellular concentration models. GFA-MLR QSAR analysis was employed to determine the optimal QSAR equation. To validate the predictability of the $IKK{\beta}$ QSAR model for an external set of inhibitors, a set of ordinary differential equations and mass action kinetics were used for modeling the NF-${\kappa}B$ dynamic system. The reaction parameters were obtained from previously reported research. In the IKKb QSAR model, good cross-validated $q^2$ (0.782) and conventional $r^2$ (0.808) values demonstrated the correlation between the descriptors and each of their activities and reliably predicted the $IKK{\beta}$ activities. Using a developed simulation model of the NF-${\kappa}B$ signaling pathway, we demonstrated differences in $I{\kappa}B$ mRNA expression between normal and different inhibitory states. When the inhibition efficiency increased, inhibitor 1 (PS-1145) led to long-term oscillations. The combined computational modeling and NF-${\kappa}B$ dynamic simulations can be used to understand the inhibition mechanisms and thereby result in the design of mechanism-based inhibitors.