Biomedical subjects

Dimitar A Dobchev

Publications and source records attributed to Dimitar A Dobchev.

11 recordsLinked to original sources

PubMed2006

QSAR modeling of anti-invasive activity of organic compounds using structural descriptors.

The anti-invasive activity of 139 compounds was correlated by an artificial neural network approach with descriptors calculated solely from the molecular structures using CODESSA Pro. The best multilinear regression method implemented in CODESSA Pro was used for a pre-selection of descriptors. The resulting nonlinear (artificial neural network) QSAR model predicted the exact class for 66 (71%) of the training set of 93 compounds and 32 (70%) of validation set of 46 compounds. The standard deviation ratios for the both training and validation sets are less than unity, indicating a satisfactory predictive capability for classification of the nature of the anti-invasive activity data. The proposed model can be used for the prediction of the anti-invasive activity of novel classes of compounds enabling a virtual screening of large databases of anticancer drugs.

Algorithms↗

PubMed2006

QSAR study of antiplatelet agents.

A QSAR methodology that involves multilinear (Hansch-type) and nonlinear (ANN backpropagation) approaches was developed to correlate the antiplatelet activity of 60 benzoxazinone derivatives against factor Xa. The statistical characteristics provided by multilinear model (R2 = 0.821) indicated satisfactory stability and predictive ability, while the ANN predictive ability is somewhat superior (R2 = 0.909). The multilinear model provided insight into the main factors that modulate the inhibitory activity of the investigated compounds.

Algorithms↗

PubMed2006

Skin permeation rate as a function of chemical structure.

Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log K(p)) of a set of 143 diverse compounds. Satisfactory models were obtained by three approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical molecular descriptors, and (iii) ISIDA modeling based on fragment descriptors. The predictive abilities of the models were assessed by internal and external validations. The descriptors involved in the equations are discussed from the physicochemical point of view to illuminate the factors that influence skin permeation.

Computer Simulation↗

PubMed2006

Correlation of blood-brain penetration using structural descriptors.

Experimental blood-brain partition coefficients (logBB) for a diverse set of 113 drug molecules are correlated with computed structural descriptors using CODESSA-PRO and ISIDA programs to give statistically significant QSAR models based respectively, on molecular and on fragment descriptors. The linear correlation CODESSA-PRO five-descriptor model has correlation coefficient R2=0.781 and standard deviation s2=0.123. The 'consensus model' of ISIDA gave R2=0.872 and s2=0.047. The developed models were successfully validated using the central nervous system activity data of an external test set of 40 drug molecules.

Algorithms↗

PubMed2006

QSAR modeling of the inhibition of glycogen synthase kinase-3.

Quantitative structure-activity relationship (QSAR) models of the biological activity (pIC50) of 277 inhibitors of Glycogen Synthase Kinase-3 (GSK-3) are developed using geometrical, topological, quantum mechanical, and electronic descriptors calculated by CODESSA PRO. The linear (multilinear regression) and nonlinear (artificial neural network) models obtained link the structures to their reported activity pIC50. The results are discussed in the light of the main factors that influence the inhibitory activity of the GSK-3 enzyme.

Drug Design↗

PubMed2006

QSAR study of mosquito repellents using Codessa Pro.

Protection times provided by 31 synthetic repellents against Aedes aegypti mosquitoes were correlated with the chemical structures of these repellents using Codessa Pro software. Two statistically significant quantitative models with R2 values of ca. 0.80 are presented and discussed.

Animals↗

PubMed2006

Antimalarial activity: a QSAR modeling using CODESSA PRO software.

A quantitative structure-activity relationship (QSAR) modeling of the antimalarial activity of two diverse sets of compounds for each of two strains D6 and NF54 of Plasmodium falciparum is presented. The molecular structural features of compounds are presented by molecular descriptors (geometrical, topological, quantum mechanical, and electronic) calculated using the CODESSA PRO software. Satisfactory multilinear regression models were obtained for data sets of the D6 and NF54 strains, with R2 = 0.84 and 0.89, respectively. The models were also satisfactorily validated internally. The descriptors involved in these equations were related to the mechanism of antimalarial protection.

Animals↗

PubMed2006

Reparameterized Austin Model 1 for quantitative structure-property relationships in liquid media.

A reparameterization of the quantum-chemical AM1 (Austin Model 1) model has been carried out using a nonlinear optimization based on a modification of the Levenberg-Marquardt technique. The optimum numerical values for the one-electron resonance integral parameters (beta (s) and beta (p)) and core-core repulsion atomic parameters alpha were obtained for the elements H, C, N, O, Cl and Br using the statistical fit of a two-parameter QSPR equation for the boiling points of organic compounds. A substantially improved two-parameter correlation (R2=0.9685, s=13.48 K) was obtained by using the new optimized parameters. The QSPR equation employs two molecular descriptors, a bulk cohesiveness descriptor, [Formula: see text] and the area-weighted surface charge of hydrogen-bonding donor atom(s) in the molecule. The model developed shows remarkably accurate predictions of the normal boiling points for nine additional simple inorganic compounds. The new parameters were tested on the critical temperatures of 165 organic compounds. The new QSPR model obtained for this property was found to be statistically significantly better than the original model. [Figure: see text].

Models, Chemical↗

PubMed2005

QSAR studies on 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor.

This work is devoted to the development of quantitative structure-activity relationship (QSAR) models of the biological activity of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. The molecular features are represented by chemical descriptors that have been calculated on geometrical, topological, quantum mechanical, and electronic basis by using CODESSA PRO. The obtained models, linear (multilinear regression) and nonlinear (artificial neural network), are aimed to link the structures to their reported activity log 1/IC50. The former model can be used for physico-chemical interpretation, while the latter possesses a superior predictive ability.

Benzimidazoles↗

PubMed2005

QSAR treatment of drugs transfer into human breast milk.

A satisfactory model is developed using CODESSA PRO for the correlation and prediction of milk to plasma concentration ratios (M/P ratio) for diverse pharmaceuticals. A set of experimentally derived M/P ratio values were collected from the literature for 115 widely used pharmaceuticals. The experimental logarithmic M/P ratios were tested with more than 850 theoretical molecular descriptors including constitutional, topological, geometrical, quantum chemical, thermodynamic, and electrostatic types. Based on the data set, for 100 commonly used drugs, a seven-parameter QSAR model was derived that shows a satisfactory (R(2)=0.791) correlation between predicted and observed values of log(M/P) ratio.

Humans↗

PubMedDate not supplied

Neural networks convergence using physicochemical data.

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling. The superiority of the Levenberg-Marquardt algorithm is revealed in terms of functional dependence of the change of the neural network weights with respect to the gradient of the error propagation as well as distribution of the weight values. The prediction of the models is assessed by the error of the validation sets not used in the training process.

ATP Binding Cassette Transporter, Subfamily B, Mem↗