• 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.

• Journal of Integrative Natural Science
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• v.10 no.2
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• pp.78-84
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• 2017

CXC chemokine receptor 2 (CXCR2) is a prominent chemokine receptor on neutrophils. CXCR2 antagonist may reduce the neutrophil chemotaxis and alter the inflammatory response because the neutrophilic inflammation in the lung diseases is found to be largely regulated through CXCR2 receptor. Hence, in the present study, Hologram based Quantitative Structure Activity Relationship Study was performed on a series of CXCR2 antagonist named pyrimidine-5-carbonitrile-6-alkyl derivatives. The best HQSAR model was obtained using atoms, bonds, and chirality as fragment distinction parameter using hologram length 151 and 6 components with fragment size of minimum 4 and maximum 7. Significant cross-validated correlation coefficient ($q^2=0.774$) and non cross-validated correlation coefficients ($r^2=0.977$) were obtained. The model was then used to evaluate the six external test compounds and its $r^2_{pred}$ was found to be 0.614. Contribution map show that presence of cyclopropyl ring and its bulkier substituent's makes big contributions for improving the biological activities of the compounds. We hope that our HQSAR model and analysis will be helpful for future design of novel and structurally related CXCR2 antagonists.

• Reproductive and Developmental Biology
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• v.34 no.1
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• pp.7-13
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• 2010

The binding affinity constants ($p(Od)_{50}$) and molecular docking scores (OS) between porcine odorant binding proteins pOBP (1HQP) and pPBP (1GM6) as receptor and a series of tetrahydrofuran-2-yl (A & B) analogues as substrate, and their interactions were discussed quantitatively using three-dimensional quantitative structure-activity relationship (30-QSAR) models. The statistical qualities of the optimized CoMF A models for pOBP were better than those of the CoMSIA models. The binding affinity constants and OS between substrate and receptor molecules were dependent upon steric and hydrophobic interaction. The DS constants of the substrates into the binding site of OBP (1HQP) were bigger than those of PBP (1GM6). The resulting contour maps produced by the optimized CoMFA model were used to identify the structural features relevant to the binding affinity in binding site of pOBP.

• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3181-3187
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• 2014

A series of 3,4-dihydroquinazoline derivatives with anti-cancer activities against human colon cancer HT-29 cell were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular similarity indices analysis (CoMSIA) approaches. The most potent compound, BK10001 was used to align the molecules. As a result, the best prediction was obtained with CoMSIA combined electrostatic, hydrophobic, and hydrogen-bond acceptor fields ($q^2=0.648$, $r^2=0.882$). This model was validated by an external test set of six compounds giving satisfactory predictive $r^2$ values of 0.879. This model would guide the design of potent 3,4-dihydroquinazoline derivatives as anti-cancer agent for the treatment of human colon cancer.

• Journal of Integrative Natural Science
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• v.11 no.2
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• pp.93-100
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• 2018

Caspases, a family of cysteinyl aspartate-specific proteases plays a central role in the regulation and the execution of apoptotic cell death. Caspase-3 has been proven to be an effective target for reducing the amount of cellular and tissue damage because the activation of caspases-3 stimulates a signalling pathway that ultimately leads to the death of the cell. In this study, Hologram based Quantitative Structure Activity Relationship (HQSAR) models was generated on a series of Caspase-3 inhibitors named 3, 4-dihydropyrimidoindolones derivatives. The best HQSAR model was obtained using atoms, bonds, and hydrogen atoms (A/B/H) as fragment distinction parameter using hologram length 61 and 3 components with fragment size of minimum 5 and maximum 8. Significant cross-validated correlation coefficient ($q^2=0.684$) and non cross-validated correlation coefficients ($r^2=0.754$) were obtained. The model was then used to evaluate the eight external test compounds and its $r^2_{pred}$ was found to be 0.559. Contribution map show that presence of pyrrolidine sulfonamide ring and its bulkier substituent's makes big contributions for improving the biological activities of the compounds.

• Radiation Oncology Journal
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• v.36 no.4
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• pp.265-275
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• 2018

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

• Archives of Pharmacal Research
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• v.27 no.10
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• pp.1001-1008
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• 2004

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on 59 cinnamaldehyde analogues as Farnesyl Protein Transferase (FPTase) inhibitors were investigated using comparative molecular field analysis (CoMFA) with the PLS region-focusing method. Forty-nine training set inhibitors were used for CoMFA with two different grid spacings, $2{\AA}\;and\;1{\AA}$ Ten compounds, which were not used in model generation, were used to validate the CoMFA models. After the PLS analysis, the best predictive CoMFA model showed that the cross-validated value $(r^2_{cv})$ and the non-cross validated conventional value$(r^2_{ncv})$ are 0.557 and 0.950, respectively. From the CoMFA contour maps, the steric and electrostatic properties of cinnamaldehyde analogues can be identified and verified.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• 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.

• KSBB Journal
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• v.15 no.3
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• pp.299-306
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• 2000

Concern for the effects of toxic chemicals on the environment leads the search for better bioassay test organisms and test procedures. Photobacterium phosphoreum was used successfully as a test organism and the luminometer detection technique was an effective and simple method for determining the concentration of toxic chemicals. With EC50 a total of 14 chlorine substituted phenols benzenes and ethanes were used for the experiments. The test results showed that the toxicity to P. phosphoreum increased in the order of phenol > benzene > ethane and the toxicity also increased with the number of chlorine substitution. Quantitative structure activity relationship (QSARO) model can be used to predict EC50 to save time and endeavor. Correlation was well established with the QSAR parameters such as log P, log S and solvatochromic parameter(Vi/100 $\pi$, ${\beta}$m and am). The QSAR modeling was used with multi-regression analysis and mono-regression analysis. These analyses resulted in the following QSAR : $log EC_{50} =2.48 + 0.914 log S(n=9 R2=85.5% RE=0.378) log EC_{50}=0.35 - 4.48 Vi/100 + 2.84 \pi^* +9.46{\beta}m-4.48am (n =14 R2=98.2% RE=0.012) log EC_{50} =2.64 -1.66 log P(n=5, R2=98.8% RE=0.16) log EC_{50}=3.44 -1.09 log P(n=9 R2= 80.8% Re=0.207)$