AI-Driven Scientific Prompting and Sequential Discovery Pipeline for p53-Targeted Predictive Modeling and Therapeutic Insights in Breast Cancer Using Ursolic Acid from Tulsi

Abstract

Breast cancer remains a major global health challenge due to its molecular heterogeneity and frequent development of therapeutic resistance. One of the key molecular factors contributing to this complexity is the tumour suppressor protein p53, which plays a central role in regulating cell cycle control, DNA repair, and apoptosis. Mutations or functional inactivation of p53 are commonly observed in aggressive breast cancer subtypes, making it an important yet challenging target for therapeutic exploration. Ursolic acid, a naturally occurring pentacyclic triterpenoid found in Ocimum sanctum (Tulsi), has attracted interest due to its reported anticancer, anti-inflammatory, and pro-apoptotic properties. Its ability to interact with multiple cellular pathways suggests potential relevance in p53-associated breast cancer; however, systematic evaluation of its molecular interactions and therapeutic potential remains limited. Recent advances in artificial intelligence (AI) have transformed early-stage drug discovery by enabling efficient data integration, predictive modeling, and hypothesis generation. AI-driven approaches are particularly valuable for studying complex targets such as p53 and for prioritizing natural compounds with multi-target effects. This review proposes an AI-driven sequential discovery framework to explore the p53-targeted therapeutic potential of Tulsi-derived ursolic acid in breast cancer. By integrating AI-assisted scientific prompting, literature mining, molecular interaction prediction, and systems-level analysis, this work provides a structured, computational perspective on natural compound-based oncology research. The review aims to highlight how AIenabled pipelines can support predictive modeling, guide experimental prioritization, and contribute to precisionoriented breast cancer therapeutics.