• Analytical Science and Technology
• /
• v.28 no.4
• /
• pp.270-277
• /
• 2015

Boiling point (BP) is one of the most fundamental physicochemical properties of organic compounds to characterize and identify the thermal characteristics of target compounds. Previously developed QSPR equations, however, still had some limitation for the specific compounds, like high-energy molecules, mainly because of the lack of experimental data and less coverage. A large BP dataset of 5,923 solid organic compounds was finally secured in this study, after dedicated pre-filtration of experimental data from different sources, mostly consisting of compounds not only from common organic molecules but also from some specially used molecules, and those dataset was used to build the new BP prediction model. Various machine learning methods were performed for newly collected data based on meaningful 2D descriptor set. Results of combined check showed acceptable validity and robustness of our models, and consensus approaches of each model were also performed. Applicability domain of BP prediction model was shown based on descriptor of training set.

• Textile Coloration and Finishing
• /
• v.21 no.5
• /
• pp.21-26
• /
• 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.

• Bulletin of the Korean Chemical Society
• /
• v.33 no.3
• /
• pp.855-861
• /
• 2012

A pioneering version of an on-line management system for high-energy molecules (MS-HEMs) was developed by the ADD and BMDRC in Korea. The current system can manage the physicochemical and explosive properties of virtual and existing HEMs. The on-line MS-HEMs consist of three main routines: management, calculation, and search. The management routine contains a user-friendly interface to store and manage molecular structures and other properties of the new HEMs. The calculation routine automatically calculates a number of compositional and topological molecular descriptors when a new HEM is stored in the MS-HEMs. Physical properties, such as the heat of formation and density, can also be calculated using group additivity methods. In addition, the calculation routine for the impact sensitivity can be used to obtain the safety nature of new HEMs. The impact sensitivity was estimated in a knowledge-based manner using in-house neural network code. The search routine enables general users to find an exact HEM and its properties by sketching a 2D chemical structure, or to retrieve HEMs and their properties by giving a range of properties. These on-line MS-HEMs are expected be powerful tool for deriving novel promising HEMs.

• BMB Reports
• /
• v.31 no.5
• /
• pp.459-467
• /
• 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.

• Environmental Analysis Health and Toxicology
• /
• v.16 no.3
• /
• pp.115-120
• /
• 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.

• Journal of the Korean Chemical Society
• /
• v.45 no.5
• /
• pp.422-428
• /
• 2001

Retention relative times(RRTs) of PAH molecules and their derivatives in gas chromatography are trained and predicted in testing sets using a multiple linear regression(MLR) and an artificial neural network(ANN). The main descriptors of PAHs and their derivatives in QSRR are the square root of molecular weight(sqmw), molecular connectivity($^1{\chi}_v$), molecular dipole moment(D) and length-to-breadth ratios(L/B). The results of MLR shows that a heavy molecule has a propensity for long retention time. L/B closely related with slot model is a good descriptor in MLR. On the other hand, ANN which is not effected by the linear dependencies among the descriptors were exclusively based on molecular weight and molecular dipole moment. The variances which shows the accuracy of prediction for retention times in testing sets are 1.860, 0.206 for MLR and ANN, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

• Proceedings of the Korean Institute of Surface Engineering Conference
• /
• 2017.05a
• /
• pp.126-126
• /
• 2017

Using first principles calculations we unveil fundamental mechanism of hydrolysis reactions of two hazardous chemicals $PCl_3$ and $POCl_3$ with molecular water clusters nearby. It is found that the water molecules play a key role as a catalyst significantly lowing the activation barriers by transferring its protons to the reaction intermediates. Interestingly, torsional angles of molecular complexes at transition states are identified as a vital descriptor on the reaction rate. Analysis of charge distribution over the complexes further reinforces the finding with atomic level correlation between the torsional angle and variation of the orbital hybridization state of P in the complex. Electronic charge separation (or polarization) enhances thermodynamic stability of the activated complex at transition state and reduces the activation energy through hydrogen bonding network with water molecules nearby. Calculated potential energy surfaces (PES) for the hydrolysis reactions of $PCl_3$ and $POCl_3$ depict their two contrastingly different profiles of double- and triple-deep wells, respectively. It is ascribed to the unique double-bonding O=P in the $POCl_3$. Our results on the activation free energy show well agreements with previous experimental data within $7kcalmol^{-1}$ deviation.

• Bulletin of the Korean Chemical Society
• /
• v.32 no.11
• /
• pp.3865-3872
• /
• 2011

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima of azobenzene dyes. The entire set of 191 azobenzenes was divided into a training set of 150 azobenzenes and a test set of 41 azobenzenes according to Kennard and Stones algorithm. A seven-descriptor model, with squared correlation coefficient ($R^2$) of 0.8755 and standard error of estimation (s) of 14.476, was developed by applying stepwise multiple linear regression (MLR) analysis on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-many-out crossvalidation procedure, randomization tests, and validation through the test set.

• Environmental Mutagens and Carcinogens
• /
• v.21 no.2
• /
• pp.118-121
• /
• 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.

• Membrane and Water Treatment
• /
• v.1 no.3
• /
• pp.181-192
• /
• 2010

A linear quantitative structure-property relationship (QSPR) model is presented for the prediction of rejection in permeation through membrane. The model was produced by using the multiple linear regression (MLR) technique on the database consisting of retention data of 25 pesticides in 4 different membrane separation experiments. Among the 3224 different physicochemical, topological and structural descriptors that were considered as inputs to the model only 50 were selected using several criteria of elimination. The physical meaning of chosen descriptor is discussed in detail. The accuracy of the proposed MLR models is illustrated using the following evaluation techniques: leave-one-out cross validation procedure, leave-many-out cross validation procedure and Y-randomization.