• Title, Summary, Keyword: molecular descriptors

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Optimization of Neural Networks Architecture for Impact Sensitivity of Energetic Molecules

  • Cho, Soo-Gyeong;No, Kyoung-Tai;Goh, Eun-Mee;Kim, Jeong-Kook;Shin, Jae-Hong;Joo, Young-Dae;Seong, See-Yearl
    • Bulletin of the Korean Chemical Society
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    • v.26 no.3
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    • pp.399-408
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    • 2005
  • We have utilized neural network (NN) studies to predict impact sensitivities of various types of explosive molecules. Two hundreds and thirty four explosive molecules have been taken from a single database, and thirty nine molecular descriptors were computed for each explosive molecule. Optimization of NN architecture has been carried out by examining seven different sets of molecular descriptors and varying the number of hidden neurons. For the optimized NN architecture, we have utilized 17 molecular descriptors which were composed of compositional and topological descriptors in an input layer, and 2 hidden neurons in a hidden layer.

Primary Screening of QSAR Molecular Descriptors for Genotoxicity Prediction of Drinking Water Disinfection Byproducts (DBPs), Chlorinated Aliphatic Compounds

  • Kim, Jae-Hyoun;Jo, Jin-Nam;Jin, Byung-Suk;Lee, Dong-Soo;Kim, Ki-Tae;Om, Ae-Son
    • Environmental Mutagens and Carcinogens
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    • v.21 no.2
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    • pp.113-117
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    • 2001
  • The screening of various molecular descriptors for predicting carcinogenic, mutagenic and teratogenic activities of chlorinated aliphatic compounds as drinking water disinfection byproducts (DBPs) has been investigated for the application of quantitative structure-activity relationships (QSAR). The present work embodies the study of relationship between molecular descriptors and toxicity parameters of the genotoxicity endpoints for the screening of relevant molecular descriptors. The toxicity Indices for 29 compounds constituting the testing set were computed by the PASS program and active values were chosen. We investigate feasibility of screening descriptors and of their applications among different genotoxic endpoints. The correlation to teratogenicity of all 29 compounds was significantly improved when the same analysis was done with 20 alkanes only without alkene compounds. The HOMO (highest occupied molecular orbital) energy and number of Cl parameters were dominantly contributed.

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Artificial Neural Network Prediction of Normalized Polarity Parameter for Various Solvents with Diverse Chemical Structures

  • Habibi-Yangjeh, Aziz
    • Bulletin of the Korean Chemical Society
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    • v.28 no.9
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    • pp.1472-1476
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    • 2007
  • Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

Primary Screening of QSAR Descriptors to Determine Biological Activities of Stilbene Derivatives (스틸벤유도체의 생물활성도를 예측하기 위한 QSAR 분자표현자의 검색방법에 관한 연구)

  • 김재현;고동수;엄애선
    • Environmental Analysis Health and Toxicology
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    • v.16 no.3
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    • pp.115-120
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    • 2001
  • The predictive screening of various molecular descriptors for predicting cyclooxygenase inhibitor, lipooxygenase inhibitor, leucotriene synthesis inhibitor, leucotriene antagonist activities of Stilbene moieties have been investigated for the application of quantitative structure-activity relationships (QSAR). The biological activities for 36 compounds were computed by the PASS program and molecular descriptors are cited from literatures or calculated, to investigate feasibility of screening relevant descriptors and of their applications among biological endpoints. Fairly good correlations varying from 0.7828 to 0.9032 were obtained using 12 descriptors with 29 Stilbene derivatives and 5 diazo-compounds. Our studies reveal that LogKow, electron density(X), electron density (Y),4th-order valence connectivity and water solubility can be usefully employed to predict biological activities of stilbene derivatives with simple regression models.

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Quantitative Structure-Activity Relationships (QSAR) Study on C-7 Substituted Quinolone

  • Lee, Geun U;Gwon, Sun Yeong;Hwang, Seon Gu;Lee, Jae Uk;Kim, Ho Jing
    • Bulletin of the Korean Chemical Society
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    • v.17 no.2
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    • pp.147-152
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    • 1996
  • To see the quantitative relationship between the structures of the C-7 substituted quinolones and their antibacterial activities, theoretical parameters such as the molecular van der Waals volume, surface area and some electrostatic parameters based on the molecular electrostatic potential, which represent lipophilicity, and some quantum mechanical parameters are introduced as descriptors. The sixteen substituted quinolone derivatives and twenty bacteria are used for the study. It is found that the QSARs of C-7 substituted quinolones are obtained for eleven bacteria and our descriptors are more useful for Gram positive organisms than negative ones. It is also shown that molecular surface area (or molecular Waals volume) of the C-7 substituent and net charge of C-7 atom of the quinolones are the descriptors of utmost importance.

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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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    • v.26 no.12
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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.

Linear Correlation Equation for Retention Factor of Nucleic Acid Using QSPR

  • Zheng, Jinzhu;Han, Soon-Koo;Row, Kyung-Ho
    • Bulletin of the Korean Chemical Society
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    • v.26 no.4
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    • pp.629-633
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    • 2005
  • In the reversed-phase chromatography, the retention time of sample was investigated based on the molecular structure of compound. Several descriptors that were related to retention factors were selected, and then the values of descriptors were calculated with several softwares. The effect of retention factor was measured with calculated values, and the results were obtained that each descriptors of molecular structure of compound have different effect on the retention factor. Therefore, the empirical equation for seven types of descriptors considered was obtained, and it has high values of correlation coefficient. Furthermore, the experimental data and calculated values have good agreement.

Utility of Structural Information to Predict Drug Clearance from in Vitro Data

  • Lee, So-Young;Kim, Dong-Sup
    • Interdisciplinary Bio Central
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    • v.2 no.2
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    • pp.3.1-3.4
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    • 2010
  • In the present research, we assessed the utility of the structural information of drugs for predicting human in vivo intrinsic clearance from in vitro intrinsic clearance data obtained by human hepatic microsome experiment. To compare with the observed intrinsic clearance, human intrinsic clearance values for 51 drugs were estimated by the classical methods using in vivo-in vitro scale-up and by the new methods using the in vitro experimental data and selected molecular descriptors of drugs by the forward selection technique together. The results showed that taking consideration of molecular descriptors into prediction from in vitro experimental data could improve the prediction accuracy. The in vitro experiment is very useful when the data can estimate in vivo data accurately since it can reduce the cost of drug development. Improvement of prediction accuracy in the present approach can enhance the utility of in vitro data.

Prediction Models of P-Glycoprotein Substrates Using Simple 2D and 3D Descriptors by a Recursive Partitioning Approach

  • Joung, Jong-Young;Kim, Hyoung-Joon;Kim, Hwan-Mook;Ahn, Soon-Kil;Nam, Ky-Youb;No, Kyoung-Tai
    • Bulletin of the Korean Chemical Society
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    • v.33 no.4
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    • pp.1123-1127
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    • 2012
  • P-gp (P-glycoprotein) is a member of the ATP binding cassette (ABC) family of transporters. It transports many kinds of anticancer drugs out of the cell. It plays a major role as a cause of multidrug resistance (MDR). MDR function may be a cause of the failure of chemotherapy in cancer and influence pharmacokinetic properties of many drugs. Hence classification of candidate drugs as substrates or nonsubstrate of the P-gp is important in drug development. Therefore to identify whether a compound is a P-gp substrate or not, in silico method is promising. Recursive Partitioning (RP) method was explored for prediction of P-gp substrate. A set of 261 compounds, including 146 substrates and 115 nonsubstrates of P-gp, was used to training and validation. Using molecular descriptors that we can interpret their own meaning, we have established two models for prediction of P-gp substrates. In the first model, we chose only 6 descriptors which have simple physical meaning. In the training set, the overall predictability of our model is 78.95%. In case of test set, overall predictability is 69.23%. Second model with 2D and 3D descriptors shows a little better predictability (overall predictability of training set is 79.29%, test set is 79.37%), the second model with 2D and 3D descriptors shows better discriminating power than first model with only 2D descriptors. This approach will be used to reduce the number of compounds required to be run in the P-gp efflux assay.

An ab Initio Predictive Study on Solvent Polarity (용매 극성도의 이론적 예측 연구)

  • Park, Min-Kyu;Cho, Soo-Gyeong
    • Journal of the Korea Institute of Military Science and Technology
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    • v.11 no.3
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    • pp.154-160
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    • 2008
  • We investigated molecular polarity by using theoretical means and comparing empirical solvent polarity. Our approach employed electrostatic potentials at the molecular surface calculated by density functional methods. A number of molecular descriptors related to molecular polarities were computed from molecular surface electrostatic potentials. Among computed molecular descriptors, the most positive electrostatic potential provided the best correlation with the empirical solvent polarities. A regression equation was developed in order to predict molecular polarities of molecules whose experimental solvent polarities were unknown. The new regression equations were utilized in estimating solvent polarities of cubane derivatives which are considered important precusors of high-energy density meterials.