Intellectual Property India Publishes Patent Application for Ai-Based Graph-Theoretic Drug Repositioning Framework For Cancer Therapy Using Topological Protein Interaction Mapping

MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641067313 A) filed by Saranya C; Mrs. P. Parameswari; Mrs. S. Aruna Devi; Mrs. S. Apshana Parvin; Mrs. S. Saranya; Dr. R. Venugopal; M. Prasannakumar; Dr. Keshav Kumar K.; and Ms. Poornavalli R on May 25, 2026, for Ai-Based Graph-Theoretic Drug Repositioning Framework For Cancer Therapy Using Topological Protein Interaction Mapping.

Inventors include Saranya C; Mrs. P. Parameswari; Mrs. S. Aruna Devi; Mrs. S. Apshana Parvin; Mrs. S. Saranya; Dr. R. Venugopal; M. Prasannakumar; Dr. Keshav Kumar K.; and Ms. Poornavalli R.

The application for the patent was published on June 19, 2026, under issue no. 25/2026.

Abstract: ABSTRACT The present invention relates to an artificial intelligence-based graph-theoretic framework for drug repositioning in cancer therapy. The framework is configured to integrate biomedical data relating to proteins, genes, cancer types, drug molecules, pathways, mutations, biomarkers, and drug-target interactions into a structured graph-based representation. In the generated graph, biological and pharmacological entities are represented as nodes, while relationships between them are represented as edges. A protein interaction mapping module identifies cancer-associated network features, including hub proteins, disease modules, bridge nodes, pathway clusters, and topologically significant molecular targets. An artificial intelligence engine analyzes the graph using machine learning, graph neural networks, link prediction, similarity scoring, clustering, and ranking techniques to predict existing drugs that may be repositioned for cancer therapy. The framework ranks candidate drugs according to network proximity, target relevance, pathway modulation potential, interaction strength, safety-related information, and confidence score. An explainable output interface provides candidate drug names, associated cancer types, predicted protein targets, affected pathways, topological reasoning, and supporting evidence. The invention enables faster and cost-effective identification of repositioned drug candidates for cancer treatment and supports researchers, clinicians, biotechnology laboratories, and pharmaceutical developers in evidence-based therapeutic discovery.

Disclaimer: Curated by HT Syndication.