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.

Using in vivo 13 C-NMR spectroscopy, the energy metabolism in rat brain has commonly been studied via infusion of 13 C-labeled substrates on a minute to hour time scale. In the present study, as a novel approach, 13 C-enriched animal chow was administered over several days and compared with a 2 h infusion of [U-13 C 6 ]-glucose. Rats received chow containing either [U-13 C 6 ]-glucose or [U-13 C]-biomass (a mixture of proteins, lipids, DNA, and carbohydrates) during 3 to 5 days. During feeding with 13 C-labeled glucose and biomass, in vivo 13 C-NMR spectroscopy was carried out daily and revealed slow but successive label incorporation into a large number of metabolites. Lipids and proteins were not significantly 13 C-enriched during a 2 h infusion of 13 C-labeled glucose, but became the most prominent resonances in the 13 C feeding experiment. Likewise, feeding with 13 C-enriched biomass led to additional 13 C-label incorporation into creatine, urea carbons and glycogen. Finally, only the acetyl moiety of N-acetyl-aspartate (NAA) became significantly enriched during the 2 h infusion experiment, whereas the aspartyl moiety remained at natural abundance levels. In the feeding experiments, however, label incorporation into all carbons of NAA could be observed. Moreover, isotopomer analysis of brain extracts revealed that the acetyl moiety of NAA in feeding experiments was always more strongly 13 C-enriched than its aspartyl moiety, suggesting that the turnover of the acetyl moiety is faster than that of the aspartyl moiety. The different enrichment kinetics of acetyl and aspartyl moiety could be explained by the existence of two different metabolic pathways reflecting the compartmentalised synthesis of NAA.

Quantitative Structure-Activity Relationship (QSAR) models are developed for three pharmacological permeabilities, i.e. two PAMPA apparent permeabilities (logP app ) at different pH values (pH 5.5 and pH 7.4) and Caco-2 cell monolayer apparent permeability (logP app (Caco-2)). The compounds are represented by chemical descriptors calculated from their constitutional, geometrical and topological structure, and quantum mechanical wave function. The obtained linear (multilinear regression) and nonlinear (artificial neural network) models link the drug structures to their reported permeabilities. Each multilinear model was tested by leave-one-out and ABC methods whereas the neural networks were assessed using the test sets. All drug structures were investigated by conformational analysis in order to find the low energy conformers.

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.

Quantitative Structure-Activity Relationship (QSAR) models were developed for blood-brain barrier and human serum albumin binding for a dataset of drugs where experimental values of both properties were available. All drugs were represented by chemical descriptors calculated from their constitutional, geometrical and topological structure, and quantum mechanical wave function. The obtained linear (multilinear regression) and nonlinear (artificial neural network) models link the drug structures to their reported properties. Also, based on the characterization of the descriptors we suggest additional criteria for the search of new active compounds. Each multilinear model was tested by leave-one-out and ABC methods. The latter method separates the all data points into three sets and predicts for each of them the property values. The former method is an iterative procedure which in each step it excludes one data point and predict its value based on the model rebuilt for the remaining data points. In addition, the predictive ability neural networks were assessed using the validation sets. All drug structures were investigated by conformational analysis in order to find the lowest energy conformers.