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Mati Karelson

Publications and source records attributed to Mati Karelson.

At least 19 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

Combination of a modified scoring function with two-dimensional descriptors for calculation of binding affinities of bulky, flexible ligands to proteins.

Bulky, flexible molecules such as peptides and peptidomimetics are often used as lead compounds during the drug discovery process. Pathophysiological events, e.g., the formation of amyloid fibrils in Alzheimer's disease, the conformational changes of prion proteins, or beta-secretase activity, may be successfully hindered by the use of rationally designed peptide sequences. A key step in the molecular engineering of such potent lead compounds is the prediction of the energetics of their binding to the macromolecular targets. Although sophisticated experimental and in silico methods are available to help this issue, the structure-based calculation of the binding free energies of large, flexible ligands to proteins is problematic. In this study, a fast and accurate calculation strategy is presented, following modification of the scoring function of the popular docking program package AutoDock and the involvement of ligand-based two-dimensional descriptors. Quantitative structure-activity relationships with good predictive power were developed. Thorough cross-validation tests and verifications were performed on the basis of experimental binding data of biologically important systems. The capabilities and limitations of the ligand-based descriptors were analyzed. Application of these results in the early phase of lead design will contribute to precise predictions, correct selections, and consequently a higher success rate of rational drug discovery.

Amyloid Precursor Protein Secretases↗
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

The classification of solvents by combining classical QSPR methodology with principal component analysis.

The results of a quantitative structure-property relationship (QSPR) analysis of 127 different solvent scales and 774 solvents using the CODESSA PRO program are presented. QSPR models for each scale were constructed using only theoretical descriptors. The high quality of the models is reflected by the squared multiple correlation coefficients that range from 0.726 to 0.999; only 18 models have R2< 0.800. This enables direct theoretical calculation of predicted values for any scale and/or for any organic solvent, including those previously unmeasured. The molecular descriptors involved in the models are classified and discussed according to (i) the origin of their calculation (i.e., constitutional, geometric, charge-related, etc.) and (ii) the commonly accepted classification of physical interactions between the solute and solvent molecules in liquid (condensed) media. A reduced matrix 774 (solvents) x 100 (solvent scales) was selected for the principal component analysis (PCA) by taking into account only the solvent scales with more than 20 experimental data points. The first 5 principal components account for 75% of the total variance. The robustness of the PCA model obtained was validated by the comparison models development for restricted submatrices of data and with the results obtained for the full data set. The total variance accounted for by the first three PCs, for the submatrices with the same number of solvent scales but different numbers of solvents, varies from 68.2% to 59.0%. This demonstrates that the total variance described by the first 3 components is essentially stable as the number of solvents involved varies from 100 to 774. Subsequently, a matrix with 703 diverse solvents and 100 solvent scales was selected for the general classification of the solvents and scales according to the scores and loadings obtained from the PCA treatment. Classification of the theoretical molecular descriptors, derived from the chemical structure alone, according to their relevance to specific types of intermolecular interaction (cavity formation, electrostatic polarization, dispersion, and hydrogen bonding) in liquid media enables a more easily comprehensible physical interpretation of the QSPR of molecular properties in liquids and solutions. The reported QSPR models for solvent scales with theoretical molecular descriptors and the results of the PCA analysis are potentially of great practical importance, as they extend the applicability of correlations with empirical solvent scales to many previously unmeasured systems.

Journal Article↗
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 modeling of blood:air and tissue:air partition coefficients using theoretical descriptors.

Human blood:air, human and rat tissue (fat, brain, liver, muscle, and kidney):air partition coefficients of a diverse set of organic compounds were correlated and predicted using structural descriptors by employing CODESSA-PRO and ISIDA programs. Four and five descriptor regression models developed using CODESSA-PRO were validated on three different test sets. Overall, these models have reasonable values of correlation coefficients (R(2)) and leave-one-out correlation coefficients (R(cv)(2)): R(2) = 0.881-0.983; R(cv)(2) = 0.826-0.962. Calculations with ISIDA resulted in models based on atom/bond sequences involving two to three atoms with statistical parameters that were similar to those of models obtained with CODESSA-PRO (R(2) = 0.911-0.974; R(cv)(2) = 0.831-0.936). A mixed pool of molecular and fragment descriptors did not lead to significant improvement of the models.

Adipose Tissue↗
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↗
PubMed2004

QSPR treatment of rat blood:air, saline:air and olive oil:air partition coefficients using theoretical molecular descriptors.

A QSPR treatment has been applied to a data set that consists of 100 diverse organic compounds to relate the logarithmic function of rat blood:air, saline:air and olive oil:air partition coefficients (denoted by log K(b:a), log K(s:a), and log K(o:a), respectively), with theoretical molecular and fragment descriptors. Three QSPR models with squared correlation coefficients of 0.881, 0.926, and 0.922, respectively, were obtained. The verification of the predictive power of these models on a test set of 33 organic chemicals that were not included in the training set gave satisfactory squared correlation coefficients: 0.791 for rat blood:air, 0.794 for saline:air and 0.846 for olive oil:air.

Animals↗
PubMed2004

QSPR of 3-aryloxazolidin-2-one antibacterials.

A QSPR treatment has been applied to a data set consisting of 60 3-aryloxazolidin-2-one antibacterials to relate the in vitro minimum inhibitory concentration (MIC) (required to inhibiting growth of S. aureus) with theoretical molecular and fragment descriptors. The treatment using codessa pro descriptors leads to a seven-parameter model with r2 = 0.820 and r2cv = 0.758.

Anti-Bacterial Agents↗
PubMed2002

Six-membered cyclic ureas as HIV-1 protease inhibitors: a QSAR study based on CODESSA PRO approach. Quantitative structure-activity relationships.

Quantitative structure-activity relationships (QSAR) for HIV-1 protease inhibitory activity of substituted tetrahydropyrimidinones have been produced using CODESSA PRO methodology and software. The best four-parameter equation (R(2)(cv)=0.847) allowed us to reveal two main structural factors which are strongly correlated with the title activity: molecular hydrophobicity and ability to form hydrogen bonds with the target enzyme.

HIV Protease Inhibitors↗
PubMed2002

QSPR models derived for the kinetic data of the gas-phase homolysis of the carbon-methyl bond.

A quantitative structure-property relationship study was carried out on the kinetic parameters of the gas-phase homolysis for 58 different C-CH3 bonds using the CODESSA program. Six-parameter models were developed for the prediction of the log k (1047 K) and the parameters of the Arrhenius equation, log A and E. These correlations were obtained by employing the theoretical molecular descriptors, derived from only the information encoded in the chemical structure of compounds.

Journal Article↗