Kinase-Bench: Comprehensive Benchmarking Tools and Guidance for Achieving Selectivity in Kinase Drug Discovery

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Pharmacology

Background:

• Developing selective kinase inhibitors is challenging due to conserved ATP binding sites.
• Optimizing docking protocols for kinase inhibitor discovery remains a significant hurdle.

Purpose of the Study:

• Introduce Kinase-Bench, a benchmark suite for advanced virtual screening to enhance kinase inhibitor selectivity and efficacy.
• Provide guidance for discovering selective kinase inhibitors through benchmarking and case studies.

Main Methods:

• Utilized a comprehensive dataset of 6875 selective ligands and 422,799 decoys for 75 kinases from ChEMBL.
• Employed Glide High-Throughput Virtual Screen and Standard Precision with customized filters targeting specific kinase interactions.
• Applied scoring functions and protein-ligand interaction filters for virtual screening.

Main Results:

• Successfully identified novel potential JAK1 inhibitors with selectivity against TYK2.
• Compound 2 (JAK1 IC50: 980.5 nM, TYK2 IC50: 4.5 μM) and Capivasertib (JAK1 IC50: 275.9 nM, TYK2 IC50: 10.9 μM) showed substantial JAK1 selectivity.
• Demonstrated the utility of tailored virtual screening protocols in identifying selective kinase inhibitors.

Conclusions:

• Kinase-Bench is a valuable tool for selective kinase drug discovery.
• Tailored virtual screening protocols significantly aid in identifying selective kinase inhibitors.
• The findings have implications for rational drug design and future therapeutic strategies.