• Proceedings of the PSK Conference
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• 2003.04a
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• pp.250.1-250.1
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• 2003

In this work, we have conducted 3D-QSAR studies on a series of acanthonic acid derivatives that act as COX-2 inhibitors, using two different methods: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). CoMFA and CoMSIA analysis of twenty five pimarane analogues produced good models with high predictive abilities. (omitted)

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.46-51
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• 2005

Mutagen X (MX) exists in our drinking water as the bi-products of chlorine disinfection. Being one of the most potent mutagen, it attracted much attention from many researchers. MX and its analogs are tested and modeled by quantitative structure activity relationship (QSAR) methods. As a result, factors affecting this class of compounds have been found to be steric and electrostatic effects. We tried to collect all the data available from the literature. The quantitative structure-activity relationship of a set of 29 MX was analyzed using Molecular Field Analysis (MFA) and Receptor Surface Analysis (RSA). The best models gave $q^{2}=0.918,\;r^{2}=0.949$ for MFA and $q^{2}=0.893,\;r^{2}=0.954$ for RSA. The models indicate that an electronegative group at C6 position of the furanone ring increases mutagenicity.

• Bulletin of the Korean Chemical Society
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• v.27 no.2
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• pp.266-272
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• 2006

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 67 molecules of 2-amino-benzothiazole-6-anilide derivatives against lymphocyte-specific protein tyrosine kinase (P56 LCK). The molecular field analysis (MFA) and receptor surface analysis (RSA) were employed for QSAR studies and the predictive ability of the model was validated by 15 test set molecules. Structure-based investigations using molecular docking simulation were performed with the crystal structure of P56 LCK. Good correlation between predicted fitness scores versus observed activities was demonstrated. The results suggested that the nature of substitutions at the 2-amino and 6-anilide positions were crucial in enhancing the activity, thereby providing new guidelines for the design of novel P56 LCK inhibitors.

• Bulletin of the Korean Chemical Society
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• v.27 no.2
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• pp.273-276
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• 2006

Three dimensional quantitative structure-activity relationship studies for a series of isatin derivatives as a nonpeptidyl caspase-3 enzyme inhibitor were investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The first approach of non-peptidyl small molecules by 3D QSAR may be useful in guiding further development of potent caspase-3 inhibitors.

• YAKHAK HOEJI
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• v.46 no.4
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• pp.219-225
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• 2002

We have performed a 3D-QSAR/CoMFA analysis of the cytotoxic activities of thirty-five 3-arylisoquinoline derivatives against SK-OV-3 tumor cell line. The results suggested that the electrostatic, steric and hydrophobic factors of 3-arylisoquinolines were strongly correlated with the antitumor activity. Considerable predictive ability (cross-validated r2 as high as 0.841) was obtained through CoMFA.

• BMB Reports
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• v.31 no.5
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• pp.459-467
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• 1998

A new set of functional group interaction energy descriptors relevant to the ACE (Angiotensin-Converting Enzyme) inhibitory peptide, QSAR (Quantitative Structure Activity Relationships), is presented. The functional group interaction energies approximate the charged interactions and distances between functional groups in molecules. The effective energies of the computationally derived geometries are useful parameters for deriving 3D-QSAR models, especially in the absence of experimentally known active site conformation. ACE is a regulatory zinc protease in the renin-angiotensin system. Therapeutic inhibition of this enzyme has proven to be a very effective treatment for the management of hypertension. The non bond interaction energy values among functional groups of six-feature of ACE inhibitory peptides were used as descriptor terms and analyzed for multivariate correlation with ACE inhibition activity. The functional group interaction energy descriptors used in the regression analysis were obtained by a series of inhibitor structures derived from molecular mechanics and semi-empirical calculations. The descriptors calculated using electrostatic and steric fields from the precisely defined functional group were sufficient to explain the biological activity of inhibitor. Application of the descriptors to the inhibition of ACE indicates that the derived QSAR has good predicting ability and provides insight into the mechanism of enzyme inhibition. The method, functional group interaction energy analysis, is expected to be applicable to predict enzyme inhibitory activity of the rationally designed inhibitors.

• 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.34 no.4
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• pp.1212-1220
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• 2013

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

• Journal of Korean Traditional Oncology
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• v.11 no.1
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• pp.95-103
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• 2006

Objective: Gami-Samhwang-San, a herbal prescription for obesity treatment, is composed of seven crude herbs such as Ephedrae Herba, Scutellariae Radix, Acori Gramineri Rhizoma, Polygalae Radix, Typhae Pollen, Armeniacae Semen, Nelumbo Folium. This study was aimed to evaluate marker substances in n-butanol fraction (SH-21-B) from Gami-Samhwang-San by high performance liquid chromatography (HPLC). And we predicted inhibition activity of major compounds of Gami-Samhwang-San using Quantitative Structure Activity Relationships (QSAR) Methods: The separation was performed on a YMC J,sphere-H80 CI8(250${\times}$4.6 mm I.D) column by gradient elution with $H_3PO_4$ buffers in acetonitrile as the moblie phase at a flow-rate of 1.0ml/min. Results: HPLC was employed to determine the quantities and the qualities of several marker substances such as ephedrine, pseudoephedirne, baicalin, ${\beta}-asarone$, tenuifoliside, naringenin, amygdalin and hyperoside in the SH-21-B. Conclusion: We suggest this results could be a useful evidence for quality control of SH-21-B.

• 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.