Home About us Contact
|
Home About us Contact | ||
|
|
||
Electronic Descriptors (electronic + descriptor)
Selected AbstractsSubstituent electronic descriptors for fast QSAR/QSPRJOURNAL OF CHEMOMETRICS, Issue 3-4 2007Bahram Hemmateenejad Abstract Substituent electronic descriptors (SED), calculated by ab initio quantum chemical methods for radical substituents, were proposed as an efficient and simple to use descriptors for use in Quantitative structure-activity/property relationships (QSPR/QSAR) studies. Twenty five SED parameters were calculated for a set of simple substituents using orbital energies, local charges, and dipole moments. Calculation of these parameters for a substituent takes much lower time comparing with that for parent molecule. Different chemical and biological data were analyzed by the SED parameters and it was found that in addition to the simplicity and speed of calculations, models obtained by SED parameters have better or comparable efficiency in relative to existing models. Copyright © 2007 John Wiley & Sons, Ltd. [source] Accurate prediction of the blood,brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modelingJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 11 2006Bahram Hemmateenejad Abstract A genetic algorithm-based artificial neural network model has been developed for the accurate prediction of the blood,brain barrier partitioning (in logBB scale) of chemicals. A data set of 123 logBB (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO-3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, logP as a measure of hydrophobicity and different topological indices were also calculated. A three-layered artificial neural network with backpropagation of an error-learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and logBB data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the logBB data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMSV and RMSCV equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the logP and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the logBB of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the logBB data. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1125,1135, 2006 [source] QSAR Analysis of 2,3-Diaryl Benzopyrans/Pyrans as Selective COX-2 Inhibitors Based on Semiempirical AM1 CalculationsMOLECULAR INFORMATICS, Issue 8 2004Sivaprakasam Prasanna Abstract Quantitative structure-activity relationship (QSAR) analysis was performed on a combined series of 2,3 diaryl benzopyrans and pyrans for their cyclooxygenase-2 (COX-2) inhibition. QSAR investigations based on semiempirical, Austin Model-1 (AM1) calculations reveal that electronic and hydophobic interactions are primarily responsible for COX-2 enzyme-ligand interaction. The derived QSAR model aided by residual analysis demonstrated that the COX-2 inhibitory activity is highly correlated with the electronic descriptors, lowest unoccupied molecular orbital (ELUMO), Dipole-Z and hydrophobicity of the molecules. The conclusion can be drawn that more hydrophobic, electron-withdrawing substituents at 3rd aromatic ring of the lead structure improves activity. The lesser the Z component the ligand has, the more correct its orientation towards the COX-2 binding site. The derived QSAR model shows good internal (exemplified through leave one out-q2=0.786) and external (r=0.5737) predictive ability for a test set and can be used in designing better selective COX-2 inhibitors among these congeners in future. [source] Comparative QSAR Studies on Toxicity of Phenol Derivatives Using Quantum Topological Molecular Similarity IndicesCHEMICAL BIOLOGY & DRUG DESIGN, Issue 5 2010Bahram Hemmateenejad Quantitative structure activity relationship (QSAR) analyses using a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices were operated to describe and compare the mechanisms of toxicity of phenols toward five different strains (i.e., Tetrahymena pyriformis, L1210 Leukemia, Pseudomonas putida, Raja japonica and Cucumis sativus). The appropriate QSAR models for the toxicity data were obtained separately employing partial least squares (PLS) regression combined with genetic algorithms (GA), as a variable selection method. The resulting QSAR models were used to identify molecular fragments of phenol derivatives whose electronic properties contribute significantly to the observed toxicities. Using this information, it was feasible to discriminate between the mechanisms of action of phenol toxicity to the studied strains. It was found that toxicities of phenols to all strains, except with L1210 Leukemia, are significantly affected by electronic features of the phenolic hydroxyl group (C-O-H). Meanwhile, the resulting models can describe the inductive and resonance effects of substituents on various toxicities. [source] | ||||||||||