Discovery and Characterization of Novel Receptor-Interacting Protein Kinase 1 Inhibitors Using Deep Learning and Virtual Screening.

Receptor-interacting protein kinase 1 (RIPK1) serves as a critical mediator of cell necroptosis and represents a promising therapeutic target for various human neurodegenerative diseases and inflammatory diseases. Nonetheless, the RIPK1 inhibitors currently reported are inadequate for clinical research due to suboptimal inhibitory activities or lack of selectivity. Consequently, there is a need for the discovery of novel RIPK1 kinase inhibitors. In this study, we integrated a deep learning model, specifically the fingerprint graph attention network (FP-GAT), with molecular docking-based virtual screening to identify potential RIPK1 inhibitors from a library comprising 13 million compounds. Out of 43 compounds procured, two compounds (designated as 24 and 41) demonstrated enzyme inhibition activity exceeding 50% at a concentration of 10 μM against RIPK1. The half-maximal inhibitory concentrations (IC₅₀) for compounds 24 and 41 were determined to be 2.01 and 2.95 μM, respectively. Furthermore, these compounds exhibited protective effects in an HT-29 cell model of TSZ-induced necroptosis, with half-maximal effective concentrations (EC₅₀) of 6.77 μM for compound 24 and 68.70 μM for compound 41. Finally, molecular dynamics simulations and binding free energy calculations were conducted to elucidate the molecular mechanism of compounds 24 and 41 binding to RIPK1. The results show that Met92, Met95, Ala155, and Asp156 are key residues for novel RIPK1 inhibitors. In summary, this work discovered two hit compounds targeting RIPK1, which can be further structurally modified to become promising lead compounds.