Design and experimental validation of the oxazole and thiazole derivatives as potential antivirals against of human cytomegalovirus.

QSAR studies of a set of previously synthesized azole derivatives tested against human cytomegalovirus (HCMV) were performed using the OCHEM web platform. The predictive ability of the classification models has a balanced accuracy (BA) of 73-79%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA = 76-83%). The models were applied to screen a virtual chemical library with expected activity of compounds against HCMV. The five most promising new compounds were identified, synthesized and their antiviral activities against HCMV were evaluated in vitro. Two of them showed some activity against the HCMV strain AD169. According to the results of docking analysis, the most promising biotarget associated with HCMV is DNA polymerase. The docking of the most active compounds 1 and 5 in the DNA polymerase active site shows calculated binding energies of -8.6 and -7.8 kcal/mol, respectively. The ligand's complexation was stabilized by the formation of hydrogen bonds and hydrophobic interactions with amino acids Lys60, Leu43, Ile49, Pro77, Asp134, Ile135, Val136, Thr62 and Arg137.

ETM-ANN approach application for thiobenzamide and quinolizidine derivatives.

The structure anti-influenza activity relationships of thiobenzamide and quinolizidine derivatives, being influenza fusion inhibitors, have been investigated using the electronic-topological method (ETM) and artificial neural network (ANN) method. Molecular fragments specific for active compounds and breaks of activity were calculated for influenza fusion inhibitors by applying the ETM. QSAR descriptors such as molecular weight, E(HOMO), E(LUMO), ΔE, chemical potential, softness, electrophilicity index, dipole moment, and so forth were calculated, and it was found to give good statistical qualities (classified correctly 92%, or 48 compounds from 52 in training set, and 69% or 9 compounds from 13 in the external test set). By using multiple linear regression, several QSAR models were performed with the help of calculated descriptors and the compounds activity data. Among the obtained QSAR models, statistically the most significant one is the one of skeleton 1 with R(2) = 0.999.

Combined electronic-topological and neural networks study of some hydroxysemicarbazides as potential antitumor agents.

Structure-activity relationships study was performed for a series of Schiff bases hydroxysemicarbazide as potential antitumor agents by using the electronic-topological method combined with neural networks (ETM-NN). Data for the approach were obtained from conformational and quantum-chemical calculations and arranged first as matrices called electronic-topological matrices of contiguity, by one for each compound. Then specific molecular fragments were found for active compounds ('activity features') from the ETM application. After this, a system of prognosis was developed as the result of training the Kohonen self-organizing maps (SOM) by the most significant fragments.

QSAR studies and antimicrobial potential of 1,3-thiazolylphosphonium salts.

The regression QSAR models were built to predict the antimicrobial activity of new thiazole derivatives. Compounds with high predicting activity were synthesized and evaluated against Gram-positive and Gram-negative bacteria and fungi. 1,3-Thiazole-4-ylphosphonium salts 4 and 5 displayed good antibacterial properties and high antifungal activity. The predictions are in a good agreement with the experiment results, which indicate the good predictive power of the created QSAR models.

Human acetylcholinesterase inhibitors: electronic-topological and neural network approaches to the structure-activity relationships study.

The Electronic-Topological (ETM) and Neural Network methods were applied to the study of the "structure-acetylcholinesterase (AChE) inhibitor activity" relationships for a series of physostigmine and N-benzylpiperidine derivatives. Molecular fragments specific for active compounds and breaks of activity were calculated for human AChE by applying the ETM and Neural Network methods. Requirements necessary for a compound to be active were formulated; they are the result of detailed analysis of all compounds under study. A comparative study of the activity features found for human AChE was performed.

The structure-inhibitory activity relationships study in a series of cyclooxygenase-2 inhibitors: a combined electronic-topological and neural networks approach.

Structure-activity relationships study was performed for a few series of cyclooxygenase-2 (COX-2) inhibitors by using the Electronic-Topological Method combined with Neural Networks (ETM-NN). Specific molecular fragments were found for active compounds ('activity features') from both series by the ETM application. After this, a system of prognosis was developed as the result of training Kohonen's self-organizing maps (SOM) by the fragments. From the detailed analysis of all compounds under study, requirements necessary for a compound to be COX-2 inhibitor were formulated. The analysis showed that any requirements violation for a molecule resulted in a considerable decrease or even complete loss of its activity. The found activity features identified correctly different marketed drugs and new compounds that had passed pre-clinical and clinical trials; this fact confirms the workability of the system developed for the COX-2 inhibitory activity prediction.

Volume learning algorithm artificial neural networks for 3D QSAR studies.

The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies.