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, EHOMO, ELUMO, 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 R2 = 0.999

New anti-candida active nitrogen-containing bisphosphonates as inhibitors of farnesyl pyrophosphate synthase Candida albicans

In our previous work, a number of new nitrogen-containing bisphosphonates (N-BPs) with high predicted and experimental antifungal activity were presented as potential Candida albicans farnesyl pyrophos­phate synthase (FPPS) inhibitors. To confirm this hypothesis, a homologous C. albicans FPPS model with high-quality scores has been developed and used in present work to study the molecular mechanism of nit­rogen-containing bisphosphonates action as anti-Candida agents. The known FPPS inhibitors ammonium 2-(Pyridin-2-ylamino)ethylidene-1,1-bisphosphonate, risedronate and alendronate were used in molecular docking analysis. The molecular docking analysis of the new N-BPs demonstrated a number of common features of all ligand’s interaction in the active center of FPPS C. albicans. It is established that the ligands phosphonate groups are the key elements in the formation of the stable ligand-protein complexes with binding energy in a range (ΔG) from –6.6 to –7.1 kcal/mol due to a significant number of electrostatic, hydrogen and metal-acceptor bonds. It is confirmed that the new studied N-BPs 1 and 3 with high anti-Candida activity are FPPS inhibitors

НАУКОВО-МЕТОДИЧНЕ СПРЯМУВАННЯ ОРГАНІЗАЦІЇ ТА КОНТРОЛЮ САМОСТІЙНОЇ РОБОТИ СТУДЕНТІВ У НМУ ІМЕНІ О. О. БОГОМОЛЬЦЯ

The article analyzes the quality level of organisation and methodological basis of self learning, namely, among students of clinical disciplines at the Medical Faculty of Bohomolets National Medical University as an essential component of effective training of specialists. It covers the basic aspects of University internal management system that deals with quality of education as regards the implementation of scientifically grounded approaches to improve the organization of self learning among students.У статті висвітлено результати аналізу якості організації самостійної роботи студентів та її методичного забезпечення з клінічних дисциплін на медичних факультетах Національного медичного університету імені О. О. Богомольця як важливого компонента у системі ефективного управління підготовкою фахівців. Описано основні аспекти функціонування внутрішньовузівської системи управління якістю освіти у визначенні та реалізації науково обґрунтованих підходів до удосконалення організації самостійної роботи студентів.

Використання алгоритму просторового навчання штучних нейронних мереж для розпізнавання типу взаємодії нейронів по їх кроскореляційній гістограмі

An algorithm based on two types artificial neural networks (ANNs) is proposed. The first network is an associative ANN while the second network is a Self-Organizing Map of Kohonen. The results for a test set are similar to the performance of our previous expert system algorithm developed with Group Method of Data Handling (GMDH). However, while GMDH uses indices derived using the expert knowledge (and thus require considerable time and resources) the VLA process initial raw data.Для решения задачи распознавания типов взаимодействия между нейронами предложен алгоритм, основанный на использовании двух типов искусственных нейронных сетей (ИНС). Первая сеть представляет собой ассоциативную ИНС, тогда как вторая — самоорганизующиеся карты Кохонена. Результаты, полученные для тестового набора данных, подобны результатам, найденным методом группового учета аргументов (МГУА). Однако новый подход использует только исходные данные, тогда как МГУА — производные индексов, полученные дополнительным анализом начальных индексов.Для вирішення задачі розпізнавання типів взаємодії між нейронами запропоновано алгоритм, заснований на використанні двох типів штучних нейронних мереж (ШНМ). Перша мережа представляє собою асоціативну ШНМ, тоді як друга — карту Кохонена, що самоорганізується. Результати тестування на наборі даних подібні до результатів, отриманих методом групового врахування аргументів (МГВА). Однак новий підхід використовує тільки початкові дані, тоді як МГВА — похідні індексів, отримані додатковим аналізом початкових індексів

Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens.

Volume learning algorithm (VLA) artificial neural network and partial least squares (PLS) methods were compared using the leave-one-out cross-validation procedure for prediction of relative potency of xenoestrogenic compounds to the estrogen receptor. Using Wilcoxon signed rank test we showed that VLA outperformed PLS by producing models with statistically superior results for a structurally diverse set of compounds comprising eight chemical families. Thus, CoMFA/VLA models are successful in prediction of the endocrine disrupting potential of environmental pollutants and can be effectively applied for testing of prospective chemicals prior their exposure to the environment

A review of recent advances towards the development of QSAR models for toxicity assessment of ionic liquids.

center dot Ionic liquids (Its) are considered as an alternative to traditional organic solvents due to their unique physical and chemical properties. On the one hand, they have promising solvating characteristics, on the other hand, they are considered as environmentally friendly "green" solvents. Recent studies of ILs toxicity however questioned the safety of ILs.center dot Assessment of the toxicity of ILs based on laboratory testing is time-consuming and requires significant resources. Complementing this task by applying computational methods is an option for filling data gaps and allows predicting the toxicity of ILs that lack experimental data. Development and application of quantitative structure activity relationships (QSARs) for innovative design of safe-by-design Its became recently a research priority. In this review, we summarize the current knowledge on development of in silico models in predicting and classifying the hazards of ILs. In addition, we discuss biodegradability of ILs and assessment of mechanisms of toxicity of ILs based on the reported models