HERG potassium channels have a critical role in the normal electrical activity of the heart. The blockade of hERG channels in heart cells can result in a potentially fatal disorder called long QT syndrome. HERG channels can be blocked by compounds with diverse structures belonging to several drug classes. Presented herein are generative (Generative Topographic Maps) and discriminative (Support Vector Machines) classification models to categorize the compounds in silico into active and inactive classes by using different types of descriptors. The predictive performance of discriminative and generative classification models has been compared. Here, the possibility of using Generative Topographic Maps as an approach for applicability domain analysis and to generate probability-based descriptors was demonstrated to our knowledge for the first time. Comparison of obtained results with the models developed by other teams on the same data set has been performed.
Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model’s applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation.
The paper presents a decision-making method for a quantitative income estimation depending on the intensity of the future tourist flow, as a complex indicator reflecting the level of the tourist market in a region or in a separate object (a hotel complex, sanatorium, tourist base, etc.). The authors proposed to use a three-level economic and mathematical model as a practical implementation of the hotel complex room stock management process. Each its level corresponds to a specific task. At the first level it is a pre-forecast study, substantiation and selection of forecasting models. At the second it is a forecast model and the quantitative value of the predicted indicator. At the third level it is a model tohelp a decision maker (DM) with decision making, i.e., a decision tree is applied as a tool. Thus, the authors present a complete system of models and methods of decision support. The results of pre-forecast analysis, development of predictive models, building, adaptation and implementation of top-level economic and mathematical models will help decision makers to make effective management decisions. There by the maneuver material resources, choose sales technologies and search for economic solutions, including in tourism recreational production activities.
В статье обсуждаются основные направления применения методов математического моделирования в образовательном процессе. Авторами рассматриваются адаптивные инструменты моделирования, которые позволяют реализовать принципы индивидуального процесса обучения. Для этого предлагается использовать возможности программного комплекса «Advanced Tester» для диагностики уровня учебных достижений школьников. Данный программный модуль основан на методологии соответствия Галуа и математическом аппарате импликативных матриц, которые позволяют моделировать индивидуальные траектории обучения оптимальным образом. Для проверки эффективности на практике был выбран предметный материал школьных курсов физики и информатики. С одной стороны, это обусловлено тем, что авторам необходимо выявить на практике специфику применения данного подхода. А с другой стороны, обосновать инвариантность методологии соответствия Галуа относительно предметной области. Апробация проводится на двух независимых выборках обучаемых в Смоленском физико-математическом лицее при МИФИ и физико-математической школе при Смоленском государственном университете. Выводы авторов находят свое отражение в результатах экспериментальной деятельности, анализ которых свидетельствует об эффективности применения методологии соответствия Галуа для диагностики знаний школьников. Актуальность статьи связана с возможностью реализации индивидуальных запросов учащихся в рамках группового школьного обучения с помощью методов математического моделирования учебных ситуаций.Ключевые слова: диагностика, образовательный процесс, математическое моделирование, соответствие Галуа, информационно-коммуникационные технологии, автоматизированные системы обучения
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