Structure-Based Identification of Small-Molecule RIPK1 Inhibitors for Alzheimer’s Disease
Gabriel Fang
Hunter College High School, New York, USA
Publication date: July 10, 2026
Hunter College High School, New York, USA
Publication date: July 10, 2026
DOI: http://doi.org/10.34614/JIYRC2026I20
ABSTRACT
Alzheimer’s disease (AD) is a neurodegenerative disease with limited effective therapeutics. Recent studies revealed that receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is an important regulator of necroptosis and neuroinflammatory signaling, and proposed RIPK1 as an attractive therapeutic target for Alzheimer’s disease. I implemented a structure-based computational pipeline that integrated binding-site prediction, pharmacophore-based virtual screening, molecular docking, ADME evaluation, and toxicity prediction to identify potential RIPK1 inhibitors. First, potential binding sites for RIPK1 were predicted by two binding-site prediction methods. Virtual screenings using pharmacophores and molecular docking were conducted to estimate ligand binding affinity. Drug-likeness and pharmacokinetic properties were assessed by Lipinski’s rule of five and ADME profiles. Candidate compounds were also assessed for predicted toxicity based on computational models. I identified two candidate compounds predicted to inhibit RIPK1. These two candidate compounds had desirable drug-like properties, such as excellent pharmacophore alignment with RIPK1, favorable docking interactions, good ADME properties, compliance with Lipinski’s rule of five, and low predicted toxicity. These results further support RIPK1 as an attractive target for Alzheimer’s disease therapeutics. Future investigations will focus on experimental validation of the candidate compounds identified in this study.
Alzheimer’s disease (AD) is a neurodegenerative disease with limited effective therapeutics. Recent studies revealed that receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is an important regulator of necroptosis and neuroinflammatory signaling, and proposed RIPK1 as an attractive therapeutic target for Alzheimer’s disease. I implemented a structure-based computational pipeline that integrated binding-site prediction, pharmacophore-based virtual screening, molecular docking, ADME evaluation, and toxicity prediction to identify potential RIPK1 inhibitors. First, potential binding sites for RIPK1 were predicted by two binding-site prediction methods. Virtual screenings using pharmacophores and molecular docking were conducted to estimate ligand binding affinity. Drug-likeness and pharmacokinetic properties were assessed by Lipinski’s rule of five and ADME profiles. Candidate compounds were also assessed for predicted toxicity based on computational models. I identified two candidate compounds predicted to inhibit RIPK1. These two candidate compounds had desirable drug-like properties, such as excellent pharmacophore alignment with RIPK1, favorable docking interactions, good ADME properties, compliance with Lipinski’s rule of five, and low predicted toxicity. These results further support RIPK1 as an attractive target for Alzheimer’s disease therapeutics. Future investigations will focus on experimental validation of the candidate compounds identified in this study.
