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Characterizing the Chemical Space of γ-Secretase Inhibitors and Modulators.

Ángel SantiagoDulce C Guzmán-OcampoRodrigo Aguayo-OrtizLaura Domínguez
Published in: ACS chemical neuroscience (2021)
γ-Secretase (GS) is one of the most attractive molecular targets for the treatment of Alzheimer's disease (AD). Its key role in the final step of amyloid-β peptides generation and its relationship in the cascade of events for disease development have caught the attention of many pharmaceutical groups. Over the past years, different inhibitors and modulators have been evaluated as promising therapeutics against AD. However, despite the great chemical diversity of the reported compounds, a global classification and visual representation of the chemical space for GS inhibitors and modulators remain unavailable. In the present work, we carried out a two-dimensional (2D) chemical space analysis from different classes and subclasses of GS inhibitors and modulators based on their structural similarity. Along with the novel structural information available for GS complexes, our analysis opens the possibility to identify compounds with high molecular similarity, critical to finding new chemical structures through the optimization of existing compounds and relating them with a potential binding site.
Keyphrases
  • small molecule
  • machine learning
  • deep learning
  • healthcare
  • working memory
  • risk assessment
  • social media
  • health information
  • cognitive decline
  • climate change