Kinase-Bench: Comprehensive Benchmarking Tools and\nGuidance for Achieving Selectivity in Kinase Drug Discovery
Developing\nselective kinase inhibitors remains a formidable challenge\nin drug discovery because of the highly conserved structural information\non adenosine triphosphate (ATP) binding sites across the kinase family.\nTailoring docking protocols to identify promising kinase inhibitor\ncandidates for optimization has long been a substantial obstacle to\ndrug discovery. Therefore, we introduced “Kinase-Bench,”\na pioneering benchmark suite designed for an advanced virtual screen,\nto improve the selectivity and efficacy of kinase inhibitors. Our\ncomprehensive data set includes 6875 selective ligands and 422,799\ndecoys for 75 kinases, using extensive bioactivity and structural\ndata from the ChEMBL database and decoys generated by the Directory\nof Useful Decoys-Enhanced version. Our benchmarking sets and retrospective\ncase studies were designed to provide useful guidance in discovering\nselective kinase inhibitors. We employed a Glide High-Throughput Virtual\nScreen and Standard Precision complemented by three scoring functions\nand customized protein–ligand interaction filters that target\nspecific kinase residue interactions. These innovations were successfully\nimplemented in our virtual screen efforts targeting JAK1 inhibitors,\nachieving selectivity against its family member, TYK2. Consequently,\nwe identified novel potential hits: Compound <b>2</b> (JAK1\nIC<sub>50</sub>: 980.5 nM, TYK2 IC<sub>50</sub>: 4.5 μM) and\nthe approved pan-AKT inhibitor Capivasertib (JAK1 IC<sub>50</sub>:\n275.9 nM, TYK2 IC<sub>50</sub>: 10.9 μM). Using the Kinase-Bench\nprotocol, both compounds demonstrated substantial JAK1 selectivity,\nmaking them strong candidates for further investigation. Our pharmaceutical\nresults underscore the utility of tailored virtual screen protocols\nin identifying selective kinase inhibitors with substantial implications\nfor rational drug design. Kinase-Bench offers a robust toolset for\nselective kinase drug discovery with the potential to effectively\nguide future therapeutic strategies effectively.
Publication Details
- Journal
- Figshare
- Published
- 2024-12-02
- DOI
- https://doi.org/10.1021/acs.jcim.4c01830.s001
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00