Kinase-Centric Computational Drug Development

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Kinases are among the most studied drug targets in industry and academia, due to their involvement in a majority of cellular processes and, upon dysregulation, in a variety of diseases including cancer, inflammation, and autoimmune disorders. The high interest in this druggable protein family triggered the generation of a large pool of data comprising sequence, structure, bioactivity, and mutation data. Together with this continuously growing amount of available data, comes the need as well as the opportunity to organize, analyze, and utilize this data in order to aid the design of novel, active, and potentially selective kinase inhibitors. In this chapter, we provide a comprehensive overview of kinase-centric data resources and tools that can be utilized for computationally driven kinase research. The contents of all resources are summarized, and all platforms focused on human kinases are discussed in more detail. Furthermore, practical applications from literature and illustrative examples showcasing the aforementioned sources and tools are presented. These applications utilize sequence, structure, and bioactivity data and range from single structure analysis, sequence comparisons, binding site predictions, druggability predictions, and protein–ligand interaction fingerprinting to activity predictions using machine learning methods. Finally, a perspective is given on the unmet needs, potential pitfalls, and current developments in kinase drug design.

Original languageEnglish
Title of host publicationAnnual Reports in Medicinal Chemistry
Number of pages37
PublisherAcademic Press
Publication date1 Jan 2017
Publication statusPublished - 1 Jan 2017
Externally publishedYes
SeriesAnnual Reports in Medicinal Chemistry

    Research areas

  • Activity, Binding sites, Bioactivity, Computational tools, Drug design, Druggability, In silico screening, Kinase inhibitors, Kinases, Kinome, Machine learning, Polypharmacology, Protein–ligand interactions, Selectivity, Sequence, Structure, Target assessment

ID: 199352278