• Bulletin of the Korean Chemical Society
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• 제29권3호
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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.

• 통합자연과학논문집
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• 제10권2호
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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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• 제34권1호
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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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• 제35권11호
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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.

• 통합자연과학논문집
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• 제11권2호
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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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• 제36권4호
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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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• 제27권10호
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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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• 제54권5호
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• pp.621-629
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• 2016

EU의 REACH 제도 도입에 따라 각종 화학물질에 대한 독성 및 활성 정보 확보를 위해 화학물질의 분자구조 정보를 기반으로 화학물질의 독성 및 활성을 예측하는 정량적구조활성관계(QSAR)에 대한 연구가 최근 활발히 진행되고 있다. QSAR 모델에 사용되는 분자표현자는 매우 다양하기 때문에 화학물질의 물성 및 활성을 잘 표현할 수 있는 주요한 분자표현자를 선택하는 과정은 QSAR 모델 개발에 있어 중요한 부분이다. 본 연구에서는 화학물질의 분자구조 정보를 나타내는 주요 분자표현자의 통계적 선택 방법과 부분최소자승법(Partial least square: PLS) 기반의 새로운 QSAR 모델을 제안하였다. 제안된 QSAR 모델은 130종의 폴리염화바이페닐(Polychlorinated biphenyl: PCB)에 대한 분배계수(log P)와 14종의 PCBs에 대한 반수 치사 농도(Lethal concentration 50%: $LC_{50}$) 예측에 사용되고, 제안된 QSAR 모델 예측 정확도는 기존의 OECD QSAR Toolbox에서 제공하는 QSAR 모델과 비교하였다. 관심 화학물질의 분자표현자와 활성정보 간의 높은 상관관계를 갖는 주요 분자표현자를 선별하기 위해서, 상관계수(r)와 variable importance on projections (VIP)기법을 적용하였으며, 화학물질의 독성 및 활성정보를 예측하기 위해 선별된 분자표현자와 활성정보를 이용해 부분최소자승법(PLS)를 사용하였다. 회귀계수($R^2$)와 prediction residual error sum of square (PRESS)을 이용한 성능평가결과, 제안된 QSAR 모델은 OECD QSAR Toolbox의 QSAR 모델보다 PCBs의 log P와 $LC_{50}$에 대하여 각각 26%, 91% 향상된 예측력을 나타내었다. 본 연구에서 제안된 계산독성학 기반의 QSAR 모델은 화학물질의 독성 및 활성정보에 대한 예측력을 향상시킬 수 있고 이러한 방법은 유독 화학물질의 인체 및 환경 위해성 평가에 기여할 것으로 판단된다.

• 한국융합학회논문지
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• 제10권3호
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• pp.23-30
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• 2019

본 연구는 당뇨병 치료제 후보약물 정보를 이용하여 항당뇨에 영향을 미치는 물질구조를 발견하는데 목적이 있다. 정량적구조 활성관계를 이용한 기계 학습 모델을 만들고 부분최소자승 알고리즘을 통해 실험데이터 별로 결정계수를 파악한 후 변수중요도척도를 활용하여 주요 분자표현자를 도출하였다. 연구 결과, 후보약물 구조정보를 반영한 molecular access system fingerprint 데이터로 분석한 결과가 in vitro 데이터를 이용한 분석 결과보다 설명력이 높았으며, 항당뇨에 영향을 미치는 주요 분자표현자 역시 다양하게 도출할 수 있었다. 제안된 항당뇨 예측 및 주요인자 분석 방법을 활용한다면 유사한 과정을 반복 실험하는 기존 신약개발 방식과는 달리, 많은 비용과 시간이 소요되는 후보물질 스크리닝 (screening) 기간을 최소화하고, 신약개발 탐색기간도 단축하는 계기가 될 수 있을 것으로 기대한다.

• KSBB Journal
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• 제15권3호
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• pp.299-306
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• 2000

발광미생물 (luminescent bacteria)인 P. phosphoreum을 이용한 수계의 환경독성물질로 지정된 ethane, benzene, phenol류에 chlorine이 치환된 l47~의 독성강도를 생체발광의 50%저하시키 는 독성농도인 ECso값을 통한 생물학적 정량을 하였을 때 phenol) benzene) ethane 의 순서로 독성깅도가 높게 산출되 어졌으며, 특히 지환된 chlo괴ne의 수가 증가할수록 독성강도가 강하다는 것을 알 수 있었다. 또한 산출된 ECso값을 이용허여 독성물질들의 물려화학적 parameter특성인 octan이(water 분할계 수 (log P), 용해도 (log S) 및 solvatochromic parameter의 떤관쟁 을 QSAR 모탤링하였으며 실힘을 통하지 않고, 독성의 독성강도 를 예측할 수 있는 회기식을 다음과 같이 산출하였다. $log EC_{50} =2.48 + 0.914 log S(n=9 R2=85.5% RE=0.378) log EC_{50}=0.35 - 4.48 Vi/100 + 2.84 \pi^* +9.46{\beta}m-4.48am (n =14 R2=98.2% RE=0.012) log EC_{50} =2.64 -1.66 log P(n=5, R2=98.8% RE=0.16) log EC_{50}=3.44 -1.09 log P(n=9 R2= 80.8% Re=0.207)$. QSAR 모델은 QSAR 검증식을 통하여 확인된 다중회기식을 이용함으로 실험하지 않은 독성물 질이 갖는 물리화학적인 특성을 대입하여 log Eeso값을 예측할 수 있으므로 경제적, 시간적으로 이익을 얻을 수 있는 모델이다.