Authors
Feier Ma, Qi Li, Sirui Zhou, Xiaoya Li, Jin-Kui Yang
Published in
PLoS computational biology. Volume 22. Issue 7. Pages e1014464. Jul 10, 2026. Epub Jul 10, 2026.
Abstract
Cathepsin L (CTSL) is a prominent therapeutic target for kidney injury, yet clinically available CTSL inhibitors remain limited. Here, we developed an artificial intelligence (AI)-assisted discovery strategy to identify novel CTSL inhibitors from a natural products library. Through a robust deep learning model and molecular docking, we screened 200 molecules from natural products library for experimental validation. Active candidates were further analyzed by molecular dynamics simulations to characterize binding modes and key CTSL-ligand interaction networks, followed by evaluation of therapeutic efficacy in kidney injury-relevant models. At a concentration of 100 µM, we found that 43 of them exhibited more than 50% inhibition of CTSL. Notably, nine molecules displayed over 90% inhibition and exhibited concentration-dependent effects. Molecular dynamics simulations indicated that Kuwanon G (KG), Iberverin, and Wighteone stably bind within the CTSL active site. In human renal cells, KG attenuated high glucose and high lipid induced inflammatory and injury responses. Collectively, these findings identify new CTSL inhibitors with therapeutic potential for renal injury and underscore the utility of AI-assisted strategies in accelerating drug discovery.
PMID:
42430439
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 7
- Comments 0