Investigating protein folding as a target for neurodegenerative disorders using a biological activity prediction model based on deep learning

3rd International Conference on Chemo and BioInformatics, Kragujevac, September 25-26. 2025. (pp. 319-322) 

 

АУТОР(И) / AUTHOR(S): Thomas Papikinos, Marios Krokidis, Aris Vrahatis, Panagiotis Vlamos Themis Exarchos

 

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DOI:  10.46793/ICCBIKG25.319P

САЖЕТАК / ABSTRACT:

Protein misfolding is a hallmark feature of neurodegenerative disorders (NDs), playing a central role in their pathogenesis by disrupting cellular proteostasis and leading to neuronal degeneration. Molecular chaperones, such as heat shock proteins, are crucial in maintaining protein homeostasis by assisting in proper protein folding, preventing aggregation, and facilitating the clearance of misfolded proteins. A machine learning framework based on neural networks has been developed that predicts how much a given compound can enhance the activity of a target protein. This approach leverages large-scale biological data to connect chemical space with functional outcomes, providing a systematic way to explore therapeutic potential across diverse compounds. The model can be applied to screen drug libraries for compounds that increase the activity of ND-related chaperones, potentially offering therapeutic effects and also aims to aid in the acceleration of drug repurposing efforts for NDs, contributing to a better understanding of therapeutic options targeting protein homeostasis.

КЉУЧНЕ РЕЧИ / KEYWORDS:

drug repurposing, neurodegenerative disorders, protein folding, biological activity prediction, drug-target interaction prediction

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This work was partially supported by the framework of the Action ‘Flagship Research Projects in challenging interdisciplinary sectors with practical applications in Greek industry’, implemented through the National Recovery and Resilience Plan Greece 2.0 and funded by the European Union—NextGenerationEU (project code: TAEDR-0535850).

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