Dimitar A. Dobchev

Synthesis and bioassay of improved mosquito repellents predicted from chemical structure

Mosquito repellency data on acylpiperidines derived from the U.S. Department of Agriculture archives were modeled by using molecular descriptors calculated by CODESSA PRO software. An artificial neural network model was developed for the correlation of these archival results and used to predict the repellent activity of novel compounds of similar structures. A series of 34 promising N-acylpiperidine mosquito repellent candidates (4a–4q′) were synthesized by reactions of acylbenzotriazoles 2a–2p with piperidines 3a–3f. Compounds (4a–4q′) were screened as topically applied mosquito repellents by measuring the duration of repellency after application to cloth patches worn on the arms of human volunteers. Some compounds that were evaluated repelled mosquitoes as much as three times longer than N,N-diethyl-m-toluamide (DEET), the most widely used repellent throughout the world. The newly measured durations of repellency were used to obtain a superior correlation equation relating mosquito repellency to molecular structure.

Novel Carboxamides as Potential Mosquito Repellents

ABSTRACT A model was developed using 167 carboxamide derivatives, from the United States Department of Agriculture archival database, that were tested as arthropod repellents over the past 60 yr. An artificial neural network employing CODESSA PRO descriptors was used to construct a quantitative structure-activity relationship model for prediction of novel mosquito repellents. By correlating the structure of these carboxamides with complete protection time, a measure of repellency based on duration, 34 carboxamides were predicted as candidate mosquito repellents. There were four additional compounds selected on the basis of their structural similarity to those predicted. The compounds were synthesized either by reaction of 1-acylbenzotriazoles with secondary amines or by reaction of acid chlorides with secondary amines in the presence of sodium hydride. The biological efficacy was assessed by duration of repellency on cloth at two dosages (25 and 2.5 µmol/cm2) and by the minimum effective dosage to prevent Aedes aegypti (L.) (Diptera: Culicidae) bites. One compound, (E)-N-cyclohexyl-N-ethyl-2-hexenamide, was superior to N,N-diethyl-3-methylbenzamide (deet) at both the high dosage (22 d versus 7 d for deet) and low dosage (5 d versus 2.5 d for deet). Only one of the carboxamides, hexahydro-1-(1-oxohexyl)-1H-azepine, had a minimum effective dosage that was equivalent or slightly better than that of deet (0.033 µmol/cm2 versus 0.047 µmol/cm2).

Prediction of Cell-Penetrating Peptides using Artificial Neural Networks

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.

QSAR modeling of the antifungal activity against Candida albicans for a diverse set of organic compounds.

The molecular structures of 83 diverse organic compounds are correlated by a quantitative structure-activity relationship (QSAR) to their minimum inhibitor concentrations (MIC expressed as log(1/MIC)), involving 6 descriptors with R(2)=0.788, F=47.140, s(2)=0.130. A novel QSAR development technique is utilized combining advantages of the two frequently applied methods. The topological, electronic, geometrical, and hybrid type descriptors for the compounds were calculated by CODESSA PRO software.

Rational design of a series of novel amphipathic cell-penetrating peptides

A series of novel, amphipathic cell-penetrating peptides was developed based on a combination of the model amphipathic peptide sequence and modifications based on the strategies developed for PepFect and NickFect peptides. The aim was to study the role of amphipathicity for peptide uptake and to investigate if the modifications developed for PepFect peptides could be used to improve the uptake of another class of cell-penetrating peptides. The peptides were synthesized by solid phase peptide synthesis and characterized by circular dichroism spectroscopy. Non-covalent peptide-plasmid complexes were formed by co-incubation of the peptides and plasmids in water solution. The complexes were characterized by dynamic light scattering and cellular uptake of the complexes was studied in a luciferase-based plasmid transfection assay. A quantitative structure-activity relationship (QSAR) model of cellular uptake was developed using descriptors including hydrogen bonding, peptide charge and positions of nitrogen atoms. The peptides were found to be non-toxic and could efficiently transfect cells with plasmid DNA. Cellular uptake data was correlated to QSAR predictions and the predicted biological effects obtained from the model correlated well with experimental data. The QSAR model could improve the understanding of structural requirements for cell penetration, or could potentially be used to predict more efficient cell-penetrating peptides.

QSPR study of the first and second critical micelle concentrations of cationic surfactants

Abstract Linear and nonlinear predictive models are derived for 50 ammonium and quaternary pyridinium cationic surfactants. Multilinear models were developed for both the first and second critical micelle concentrations (CMCs). Additionally, a general ANN model was deduced for the first CMC of all 50 cationic surfactants. Most of the descriptors in these models are related to the size and charge of the hydrophobic tail and to the size of the head. The multilinear model for the second CMC was more closely related to the hydrophobic domain of the surfactant than that of the first CMC. The QSPR models (linear and nonlinear) for the first CMC in this work could provide useful predictions of the CMC of structurally similar cationic surfactants.

QSPR study of critical micelle concentration of anionic surfactants using computational molecular descriptors.

A data set of 181 diverse anionic surfactants has been investigated to relate the logarithm of critical micelle concentration (cmc) to the molecular structure using Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA Pro) software. A fragment approach provided superior quantitative structure-property relationship (QSPR) models in terms of statistical characteristics and predictive ability. The regression equations provided insight into the structural features of surfactants that influence cmc. The most obvious influence on cmc was manifested by hydrophobic fragments expressed by the topological and geometrical descriptors, while the hydrophilic fragment is represented by constitutional, geometrical, and charge related descriptors. Significantly important molecular descriptors in the selected QSPR models were topological, solvational, and charge-related descriptors as the driving force of the intermolecular interactions between anionic surfactants and water.

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.

Fragment-Based Development of HCV Protease Inhibitors for the Treatment of Hepatitis C

A novel computational technology based on fragmentation of the chemical compounds has been used for the fast and efficient prediction of activities of prospective protease inhibitors of the hepatitis C virus. This study spans over a discovery cycle from the theoretical prediction of new HCV NS3 protease inhibitors to the first cytotoxicity experimental tests of the best candidates. The measured cytotoxicity of the compounds indicated that at least two candidates would be suitable further development of drugs.

Physical, Chemical, and Technological Property Correlation with Chemical Structure: The Potential of QSPR*

Correlations of simple and complex physical, and chemical, biological and technological properties with chemical structure are reviewed. When an adequate training set of structures and experimentally determined property values are available, the equations produced enable the prediction of these properties of molecules as yet synthesized or indeed as yet unknown. Frequently they also offer considerable insights into the manner in which the structure controls the property. Many further applications of this methodology can be anticipated