AI- and LLM-Driven Drug Discovery to Predictive Therapeutics: Investigating Nimbolide and Azadirachtin in Oral Squamous Cell Carcinoma Using Swalife Research Platforms
Keywords:
Oral squamous cell carcinoma, OSCC, nimbolide, azadirachtin, Azadirachta indica, neemlimonoid, NF-κB pathway, STAT3 signaling, PI3K/AKT inhibition, MAPK cascade, networkpharmacology, AI-driven drug discovery, LLM literature mining, predictive medicine, Swalife platforms, apoptosis induction, preclinical models, pharmacokinetics challengesAbstract
Oral squamous cell carcinoma (OSCC) remains a leading cause of cancer mortality, driven by dysregulated pathways such as NF-κB, STAT3, PI3K/AKT, and MAPK. Nimbolide and azadirachtin, limonoids from Azadirachta indica (neem), exhibit promising preclinical anticancer effects by modulating these pathways, inhibiting proliferation, and inducing apoptosis in OSCC models. This review synthesizes published literature evidence, highlights AI/LLM roles in discovery via Swalife platforms for network pharmacology and predictive modeling, addresses pharmacokinetics/formulation challenges, and notes the absence of clinical trials. Swalife-based AI tools are proposed to enable structured hypothesis generation and multi- target analysis.
