• Applied Biological Chemistry
• /
• v.46 no.3
• /
• pp.155-164
• /
• 2003

It was reviewed for the status of domestic research before and after 1990's for search of a new pesticides using 2D QSAR of quantitative structure-activity relationship (QSAR) methodologies (Sung, Nack-Do (2002) Development of new agrochemicals by quantitative structure-activity relationship (QSAR) methodology. Kor J. Pestic. Sci. 6, 166-174, 231-243 & 7, 1-11) which was proposed according to Hansch-Fujita equation based on the concept of biological Hammett equation.

• The Korean Journal of Pesticide Science
• /
• v.7 no.1
• /
• pp.1-11
• /
• 2003

Acoording to improvement of HTOS (high throughput organic synthesis) and HTS (high throughput screening) technique, the CoMFA (comparative molecular field analysis), CoMSIA (comparative molecular similarity indeces analysis) and molecular HQSAR (hologram quantitative structure-activity relationship) analysis techniques as methodology of computer assisted molecular design (CAMD) were introduced generally and summarized for some application cases.

• Journal of the Korean Chemical Society
• /
• v.58 no.6
• /
• pp.543-552
• /
• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Bulletin of the Korean Chemical Society
• /
• v.23 no.3
• /
• pp.417-422
• /
• 2002

Comparison studies of the Quantitative Structure Activity Relationship (QSAR) methods with new imidazo-quinolinedione derivatives were conducted using Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and the Hologram Quantitative Structure Activity Relationship (HQSAR). When the CoMFA crossvalidation value, q2, was 0.625, the Pearson correlation coefficient, r2, was 0.973. In CoMSIA, q2 was 0.52 and r2 was 0.979. In the HQSAR, q2 was 0.501 and r2 was 0.924. The best result was obtained using the CoMSIA method according to a comparison of the calculated values with the real in vitro cytotoxic activities against human ovarian cancer cell lines.

• Bulletin of the Korean Chemical Society
• /
• v.26 no.1
• /
• pp.85-90
• /
• 2005

MX and its analogs are synthesized and modeled by quantitative structure activity relationship (QSAR) study including comparative molecular field analysis (CoMFA). As a result, factors affecting this class of compounds have been found to be steric and electrostatic effects. Because hologram quantitative structure activity relationship (HQSAR) technique is based on the 2-dimensional descriptors, this is free of ambiguity of conformational selection and molecular alignment. In this study we tried to include all the data available from the literature, and modeled with the HQSAR technique. Among the parameters affecting fragmentation, connectivity was the most important one for the whole compounds, giving good statistics. Considering additional parameters such as bond specification only slightly improved the model. Therefore connectivity has been found to be the most appropriate to explain the mutagenicity for this class of compounds.

• The Korean Journal of Pesticide Science
• /
• v.6 no.3
• /
• pp.166-174
• /
• 2002

The fundamental concepts on the basis of linear free energy relationship (LFER), history of development, prediction of pharmacological effects, advantages and disadvantages, etc. according to the 2D and 3D QSAR methodologies were summarized in utilizing the quantitative structure-activity relation ship (QSAR) techniques for searching and development of new agrochemicals. Objectives, role of QSAR techniques in development process of pesticides and limitations in QSARs were discussed and introduced.

• Bulletin of the Korean Chemical Society
• /
• v.33 no.5
• /
• pp.1527-1535
• /
• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• Bulletin of the Korean Chemical Society
• /
• v.22 no.4
• /
• pp.388-394
• /
• 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.

• Food Science and Biotechnology
• /
• v.17 no.3
• /
• pp.501-507
• /
• 2008

The quantitative structure-activity relationships (QSAR) study of antioxidative anthocyanidins and their glycosides were evaluated using 4 different assays of Trolox equivalent antioxidant capacity (TEAC), superoxide radical ($O_2^{{\cdot}-}$), hydrogen peroxide ($H_2O_2$), and peroxynitrite radical ($ONOO^-$) scavenging with TSAR software. Four models were developed with significant predictive values ($r^2$ and p value), which indicated that the antioxidant activities were mainly governed by the 3-dimensional structural energy (torsional energy), constitutional properties (the number of hydroxyl and methyl groups), and electrostatic properties (heat of formation, and dipole, quadrupole, and octupole components). This QSAR approach could contribute to a better understanding of structural properties of anthocyanidins and their glycosides that are responsible for their antioxidant activities. It might also be useful in predicting the antioxidant activities of other anthocyanins.

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