Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph

Abstract

Drug repurposing (DR) offers a promising alternative to the high cost and low success rate of traditional drug development, especially for complex diseases like Alzheimer's disease (AD). This study addressed DR for AD from three key angles: (1) demonstrating how disease-specific knowledge graphs can improve DR performance, (2) evaluating the role of large language models (LLMs) in enhancing the usability and efficiency of these graphs, and (3) assessing whether Graph-of-Thoughts (GoT)-enhanced LLMs, when integrated with AD knowledge graphs, can outperform traditional machine learning and LLM-based approaches. We tested five distinct DR strategies (DR1-DR5) for AD: DR1, a machine learning method using TxGNN; DR2, a machine learning model leveraging the Alzheimer's KnowledgeBase (AlzKB); DR3, an LLM-based chatbot built on AlzKB; DR4, our ESCARGOT framework combining GoT-enhanced LLMs with AlzKB; and DR5, a general reasoning-driven LLM approach. Results showed that AlzKB significantly improved DR outcomes. ESCARGOT further enhanced performance while reducing the need for coding or advanced expertise in knowledge graph analysis. Because the architecture of AlzKB is easily adaptable to other diseases and ESCARGOT can integrate with various knowledge graph platforms, this framework offers a broadly applicable, innovative tool for accelerating drug discovery through repurposing.

Fig. 1

Study design for comparing five distinct DR strategies for Alzheimer’s disease. (DR: drug-repurposing, LLM: Large Language Model, GNN: Graph Neural Network, RotatE: Rotational Embedding, GoT: Graph-of-Thoughts. * DR drug lists generated by methodologies developed by other research teams)

Fig. 2

A The architecture of AlzKB (Alzheimer’s Knowledge Base) highlights entities and their relationships. The DR-by-LLM strategy focuses on analyzing immediate linked to AD, such as Genes and Drugs, as well as their extended connections, including Pathway, BodyPart, and DrugClasses. B Examples of AD and drug connections. B1) Disease-Gene-Drug and AD pathway: AD to gene IL1B to drug Mitoxantrone, IL1B is in AD pathway “Alzheimer Disease”; B2) Disease-Gene-Drug and AD bodypart: AD to gene PSEN1 to drug Thioridazine, PSEN1 is expressed in AD related body part “telencephalon”

Fig. 3

(A1) The ESCARGOT three-step filtering process for identifying potential drug candidates connected to genes associated with AD in Fig. 2B1 (Disease-Gene-Drug and AD pathway). The first filter ensures the candidate is connected to the AD pathway; the second filter requires that the gene-drug count exceeds that of known AD drugs; and the third filter confirms the gene has a link to a transcription factor and the drug belongs to a documented drug class. At each step, the figure shows the total number of drugs discovered, how many were previously studied, and the overlap coefficient (Eq. 1). (A2) The same three-step process applies to drug candidates linked to genes associated with AD in Fig. 2B2 (Disease-Gene-Drug and AD bodypart), except the first filter ensures the candidate is connected to a relevant AD body part. (B) Bar chart illustrating the number of novel drugs (blue) and previously studied drugs (orange), with the overlap coefficient (green line; Eq. 1) across the three-step processes in A1 and A2. (C) Precision and recall were calculated for ESCARGOT at each filtering step using four different strategies for selecting result drugs: A1 (AD pathway–based), A2 (AD bodypart–based), the union of A1 and A2, and the intersection of A1 and A2. For each strategy, the precision and recall were compared with those of TxGNN and RotatE, using the same number of top-ranked drugs based on their respective drug scores