Mechanistically explainable AI model for predicting synergistic cancer therapy combinations

Abstract

This study introduces a Large Language Model (LLM)-based framework that combines drug combination data with a knowledge graph to predict synergistic oncology drug combinations with mechanistic insights. Using a retrieval-augmented generation (RAG) approach, over 50,000 in vitro drug pair assay results and 1,631 human clinical trial or preclinical test entries were integrated to enhance predictive accuracy and explainability. Validation achieved an F1 score of 0.80, demonstrating the frameworks potential to streamline drug discovery and improve translational strategies in cancer treatment.