Data-oriented protein kinase drug discovery

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.