Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

Abstract

We will develop a novel approach to drug repurposing, utilising Natural Language Processing (NLP) and Literature Based Discovery (LBD) techniques. This will present a simplified, accessible drug repurposing pipeline using Word2Vec embeddings trained on PubMed abstracts to identify potential new medications to be repurposed. We present this approach in the context of antipsychotics, but it could be repeated for any available medication. The research is structured in three stages: 1. Identification of candidate medications using Word2Vec algorithm trained on scientific literature. 2. Empirical testing of identified candidates using a large hospital dataset to explore protective effects against disease onset. 3. Validation of findings using a second, independent dataset to assess generalizability. This method addresses limitations in current machine learning-based drug repurposing approaches, including lack of external validation and limited accessibility. By leveraging Word2Vec's ability to capture semantic relationships between words, the study aims to uncover hidden connections in medical literature that may lead to novel therapeutic discoveries. The protocol emphasizes transparency and reproducibility, utilizing publicly available electronic health record (EHR) databases for validation. This approach allows for tangible results even for researchers with limited machine learning expertise, bridging the gap between biomedical and information systems communities.