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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Imidazole-4-N-acetamide Derivatives as a Novel Scaffold for Selective Targeting of Cyclin Dependent Kinases

Cancers. 2023. Vol. 15. No. 15. Article 3766.
Rusina P., Gandalipov E., Abdyusheva Y., Panova M., Alexandra Burdenkova, Chaliy V., Brachs M., Svitanko I., Fedor Novikov

General toxicity for the organism is a major drawback of anticancer drugs. Development of new generation chemotherapeutics requires the knowledge about macromolecules critical for tumor cell viability. Among these species (called therapeutic targets), the protein kinases are the established enzymes. However, the parts of protein kinases that are supposed to be targeted by drugs can be structurally similar. Therefore, it is difficult to design the compounds that selectively bind and inactivate individual kinases, especially if the proteins comprise the evolutionarily conserved families. In the present study, a set of advanced approaches of computational chemistry and biochemistry was used for the structure-based design of new compounds to inhibit cyclin-dependent protein kinases (CDK), the enzymes mechanistically implicated in tumor biology. We demonstrated the advantages of the non-equilibrium (NEQ) thermodynamics method for a time-efficacious, accurate, and informative prediction of CDK inhibitory properties of new compounds. Importantly, NEQ-based predictions correlated with experimental testing.

Research target: Clinical Medicine Chemistry
Language: English
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Keywords: molecular modelingРак молочной железыкомпьютерное молекулярное моделированиеtyrosine kinase inhibitorsингибиторы тирозинкиназmammary cancer
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