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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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Data-oriented protein kinase drug discovery.

Elena Xerxa1, Jürgen Bajorath1

  • 1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany.

European Journal of Medicinal Chemistry
|April 18, 2024
PubMed
Summary
This summary is machine-generated.

Data science enhances early drug discovery by integrating biological screening and medicinal chemistry data. This review highlights protein kinase inhibitor development and data-driven strategies for future drug discovery efforts.

Keywords:
Data scienceDrug discoveryInhibitorsModes-of-actionMulti-target activityPolypharmacologyProtein kinasesPublic domain dataSelectivity

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Area of Science:

  • Drug discovery and development
  • Medicinal chemistry
  • Data science in pharmacology

Background:

  • Biological screening and medicinal chemistry generate vast datasets.
  • Data science approaches facilitate data integration and knowledge extraction.
  • Protein kinase (PK) drug discovery is a data-rich field.

Purpose of the Study:

  • To review the evolution of protein kinase inhibitor discovery.
  • To highlight milestone developments in PK drug discovery.
  • To discuss data-driven strategies for PK discovery.

Main Methods:

  • Literature review of PK drug discovery and development.
  • Analysis of exemplary studies in small molecular PK inhibitor development.
  • Discussion of data integration and knowledge extraction from historical data.

Main Results:

  • Significant advancements in small molecular protein kinase inhibitor (PKI) development.
  • Demonstration of data science's role in leveraging historical and public data.
  • Identification of key milestones in PK drug discovery.

Conclusions:

  • The increasing volume of biological data supports data-driven experimental design.
  • Protein kinase inhibitor discovery benefits from integrating diverse data sources.
  • Future PK discovery efforts can be enhanced by adopting data-oriented strategies.