• 제목/요약/키워드: molecular descriptors

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WebChemDB: An Integrated Chemical Database Retrieval System

  • Hou, Bo-Kyeng;Moon, Eun-Joung;Moon, Sung-Chul;Kim, Hae-Jin
    • Genomics & Informatics
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    • 제7권4호
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    • pp.212-216
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    • 2009
  • WebChemDB is an integrated chemical database retrieval system that provides access to over 8 million publicly available chemical structures, including related information on their biological activities and direct links to other public chemical resources, such as PubChem, ChEBI, and DrugBank. The data are publicly available over the web, using two-dimensional (2D) and three-dimensional (3D) structure retrieval systems with various filters and molecular descriptors. The web services API also provides researchers with functionalities to programmatically manipulate, search, and analyze the data.

Quantitative Structure-Activity Relationship(QSAR) Study of New Fluorovinyloxycetamides

  • 조두호;이성광;김범태;노경태
    • Bulletin of the Korean Chemical Society
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    • 제22권4호
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    • pp.388-394
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    • 2001
  • Quantitative Structure-Activity Relationship (QSAR) have been established of 57 fluorovinyloxyacetamides compounds to correlate and predict EC50 values. Genetic algorithm (GA) and multiple linear regression analysis were used to select the descriptors and to generate the equations that relate the structural features to the biological activities. This equation consists of three descriptors calculated from the molecular structures with molecular mechanics and quantum-chemical methods. The results of MLR and GA show that dipole moment of z-axis, radius of gyration and logP play an important role in growth inhibition of barnyard grass.

Prediction Acidity Constant of Various Benzoic Acids and Phenols in Water Using Linear and Nonlinear QSPR Models

  • Habibi Yangjeh, Aziz;Danandeh Jenagharad, Mohammad;Nooshyar, Mahdi
    • Bulletin of the Korean Chemical Society
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    • 제26권12호
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    • pp.2007-2016
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    • 2005
  • An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

PCBs 독성 예측을 위한 주요 분자표현자 선택 기법 및 계산독성학 기반 QSAR 모델 개발 (Development of QSAR Model Based on the Key Molecular Descriptors Selection and Computational Toxicology for Prediction of Toxicity of PCBs)

  • 김동우;이승철;김민정;이은지;유창규
    • 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.

순수 폴리프로필렌 섬유에 대한 소수성 염료의 흡착성과 염료의 molecular descriptor와의 상관성 분석 (Relationship Between the Affinity of Hydrophobic Dyes onto Pure Polypropylene and Molecular Descriptors of the Dyes)

  • 정종석;장경진;손송이;김태경;윤석한;김미경;홍진표
    • 한국염색가공학회:학술대회논문집
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    • 한국염색가공학회 2008년도 제38차 학술발표대회
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    • pp.120-122
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    • 2008
  • Relationship between hydrophobicity of dyes and affinity onto pure polypropylene fibers has been analyzed by using the molecular descriptor as a method to predict chemical and physical characteristics of compounds. Hydrophobicity of newly synthesized red dyes calculated by LogP which is one of molecular descriptors was increased continuously as the length of alkyl substituents increased. The dyeability onto polypropylene fibers was increased as LogP of the dyes increased and was very high when the hydrophobicity is over 6.

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음용수의 염소살균부산물(DBPs)인 염화지방족화합물의 QSAR 독성예측치에 대한 열역학적 분자표현자의 역할(II) (Screening of QSAR Descriptors for Genotoxicily Prediction of Drinking Water Disinfection Byproducts (DBPs), Chlorinated Aliphatic Compounds-The Role of Thermodynamic factors)

  • 김재현;조진남
    • 한국환경성돌연변이발암원학회지
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    • 제21권2호
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    • pp.118-121
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    • 2001
  • The predictive screening of various molecular descriptors for predicting carcinogenic, mutagenic, teratogenic and alkylation activity of chlorinated disinfection byproducts (DBPs) has been investigated for the application of quantitative structure-activity relationships (QSAR). The toxicity index for 29 compounds were computed by the PASS program and active values were employed in this study. Studies show that different descriptors account for the model equation of each genotoxic endpoint and that thermodynamic descriptors significantly played a major role on prediction of endpoints of chlorinated aliphatic compounds.

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3D-QSAR of Angiotensin-Converting Enzyme Inhibitors: Functional Group Interaction Energy Descriptors for Quantitative Structure-Activity Relationships Study of ACE Inhibitors

  • Kim, Sang-Uk;Chi, Myung-Whan;Yoon, Chang-No;Sung, Ha-Chin
    • BMB Reports
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    • 제31권5호
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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.

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Calculation and Analysis of Hydrophobicity of the Dyes Synthesized for Unmodified Polypropylene Fibers Using Molecular Descriptors

  • Kim, Tae-Kyeong;Jang, Kyung-Jin;Jeon, Seon-Hee
    • 한국염색가공학회지
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    • 제21권5호
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    • pp.21-26
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    • 2009
  • In order to analyze numerically the hydrophobicity of the new dyes synthesized for unmodified pure polypropylene fibers, the octanol-water partition coefficient (logP), which is one of molecular descriptors representing hydrophobicity of organic compounds, was obtained by a semi-empirical method using Chem3D software. For the dyes of higher logP than around 5, the affinity of the dyes towards unmodified polypropylene fiber was substantial. In contrast to the new dyes for polypropylene, conventional disperse dyes have logP values lower than 5 and exhibited poor affinity.

Prediction Partial Molar Heat Capacity at Infinite Dilution for Aqueous Solutions of Various Polar Aromatic Compounds over a Wide Range of Conditions Using Artificial Neural Networks

  • Habibi-Yangjeh, Aziz;Esmailian, Mahdi
    • Bulletin of the Korean Chemical Society
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    • 제28권9호
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    • pp.1477-1484
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    • 2007
  • Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.