Informatic challenges and advances in illuminating the druggable proteome

Drug Discov Today. 2024 Mar;29(3):103894. doi: 10.1016/j.drudis.2024.103894. Epub 2024 Jan 22.

Abstract

The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.

Keywords: machine learning; network biology; orthology; protein evolution; sequence embedding.

Publication types

  • Review

MeSH terms

  • Informatics*
  • Proteome*

Substances

  • Proteome