  The vast amount of biomedical information available today presents a
significant challenge for investigators seeking to digest, process, and
understand these findings effectively. Large Language Models (LLMs) have
emerged as powerful tools to navigate this complex and challenging data
landscape. However, LLMs may lead to hallucinatory responses, making Retrieval
Augmented Generation (RAG) crucial for achieving accurate information. In this
protocol, we present RUGGED (Retrieval Under Graph-Guided Explainable disease
Distinction), a comprehensive workflow designed to support investigators with
knowledge integration and hypothesis generation, identifying validated paths
forward. Relevant biomedical information from publications and knowledge bases
are reviewed, integrated, and extracted via text-mining association analysis
and explainable graph prediction models on disease nodes, forecasting potential
links among drugs and diseases. These analyses, along with biomedical texts,
are integrated into a framework that facilitates user-directed mechanism
elucidation as well as hypothesis exploration through RAG-enabled LLMs. A
clinical use-case demonstrates RUGGED's ability to evaluate and recommend
therapeutics for Arrhythmogenic Cardiomyopathy (ACM) and Dilated Cardiomyopathy
(DCM), analyzing prescribed drugs for molecular interactions and unexplored
uses. The platform minimizes LLM hallucinations, offers actionable insights,
and improves the investigation of novel therapeutics.
