Biological Database Mining for LLM-Driven Alzheimer’s Disease Drug Repurposing

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Abstract

INTRODUCTION

The synergy of structured knowledge and large language models (LLMs) may contribute to identifying drugs for Alzheimer’s disease (AD) drug repurposing (DR). This paper developed a software pipeline that uses LLMs to translate knowledge stored in natural language (such as in scientific texts) to an applicable DR information structure.

METHODS

AD-related entries in Gene Ontology and DrugBank were integrated into a Knowledge Graph database to inform LLM prompts. Based on the biological process impact, the LLM provided a suitability rating for DR, taking into account the inhibitory effect of drugs on AD driving processes..

RESULTS

Drugs with a high potential for DR were identified and manually reviewed, also considering adverse effects. Ripretinib and Pertuzumab (both kinase inhibitors) had the highest DR applicable rating across all iterations.

DISCUSSION

We propose retrospective analyses, considering the high-rated drugs and their effect on AD patients as a starting point for further (prospective) research.

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