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Inhibitors of histone deacetylases (HDACi) have considered as a promising class of drugs for the treatment of cancers because of their effects on cell growth, differentiation, and apoptosis. Angiogenesis play an important role in the growth of most solid tumors and the progression of metastasis. The vascular endothelial growth factor (VEGF) is a key angiogenic agent, which is secreted by malignant tumors, which induces the proliferation and the migration of vascular endothelial cells. Currently, the most promising strategy in the fight against cancer is the creation of hybrid drugs that simultaneously act on several physiological targets. In this work, a series of hybrids bearing N-phenylquinazolin-4-amine and hydroxamic acid moieties were studied as dual VEGFR-2/HDAC inhibitors using simplex representation of the molecular structure and Support Vector Machine (SVM). The total sample of 42 compounds was divided into training and test sets. Five-fold cross-validation (5-fold) was used for internal validation. Satisfactory quantitative structure—activity relationship (QSAR) models were constructed (R²_test = 0.64–0.87) for inhibitors of HDAC, VEGFR-2 and human breast cancer cell line MCF-7. The interpretation of the obtained QSAR models was carried out. The coordinated effect of different molecular fragments on the increase of antitumor activity of the studied compounds was estimated. Among the substituents of the N-phenyl fragment, the positive contribution of para bromine for all three types of activity can be distinguished. The results of the interpretation were used for molecular design of potential dual VEGFR-2/HDAC inhibitors. For comparative QSAR research we used physicochemical descriptors calculated by the program HYBOT, the method of Random Forest (RF), and on-line version of the expert system OCHEM (https://ochem.eu). In the modeling of OCHEM PyDescriptor descriptors and extreme gradient boosting was chosen. In addition, the models obtained with the help of the expert system OCHEM were used for virtual screening of 300 compounds to select promising VEGFR-2/HDAC inhibitors for further synthesis and testing.

Gesture recognition is an urgent challenge in developing systems of human-machine interfaces. We analyzed machine learning methods for gesture classification based on electromyographic muscle signals to identify the most effective one. Methods such as the naive Bayesian classifier (NBC), logistic regression, decision tree, random forest, gradient boosting, support vector machine (SVM), $k$-nearest neighbor algorithm, and ensembles (NBC and decision tree, NBC and gradient boosting, gradient boosting and decision tree) were considered. Electromyography (EMG) was chosen as a method of obtaining information about gestures. This solution does not require the location of the hand in the field of view of the camera and can be used to recognize finger movements. To test the effectiveness of the selected methods of gesture recognition, a device was developed for recording the EMG signal, which includes three electrodes and an EMG sensor connected to the microcontroller and the power supply. The following gestures were chosen: clenched fist, “thumb up”, “Victory”, squeezing an index finger and waving a hand from right to left. Accuracy, precision, recall and execution time were used to evaluate the effectiveness of classifiers. These parameters were calculated for three options for the location of EMG electrodes on the forearm. According to the test results, the most effective methods are $k$-nearest neighbors’ algorithm, random forest and the ensemble of NBC and gradient boosting, the average accuracy of ensemble for three electrode positions was 81.55%. The position of the electrodes was also determined at which machine learning methods achieve the maximum accuracy. In this position, one of the differential electrodes is located at the intersection of the flexor digitorum profundus and flexor pollicis longus, the second — above the flexor digitorum superficialis.