# Mechanistically Explainable AI Model for Predicting Synergistic Cancer Therapy Combinations

## TL;DR

This paper presents an AI model that predicts effective cancer drug combinations and explains why they work, improving drug discovery and patient outcomes.

## Contribution

A novel AI framework using LLMs and knowledge graphs to predict and explain synergistic cancer drug combinations.

## Key findings

- The model achieved an F1 score of 0.80 in validating drug combination predictions.
- Integration of 50,000 in vitro and 1631 clinical/preclinical data improved accuracy and explainability.
- The approach could accelerate drug discovery and shape AI-driven oncology policies.

## Abstract

Cancer treatment often uses drug combinations to combat tumors more effectively, but identifying synergistic pairs is challenging. This study introduces an AI framework using Large Language Models with retrieval-augmented generation to predict and explain these pairs, achieving very good performance in validation. The approach could expedite drug discovery and shape policies promoting AI in oncology for better patient outcomes.

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 1631 human clinical trial and preclinical test entries were integrated to enhance predictive accuracy and explainability. Validation achieved an F1 score of 0.80, demonstrating the framework’s potential to streamline drug discovery and improve translational strategies in cancer treatment.