AI-driven drug repurposing models using biomedical literature mining
Author(s): Veerendra Nath Jasthi
Publication #: 2508006
Date of Publication: 10.03.2022
Country: United States
Pages: 1-10
Published In: Volume 8 Issue 2 March-2022
DOI: https://doi.org/10.5281/zenodo.16883304
Abstract
Repurposing or discovering new therapeutic application of old drugs is cheaper and more time-saving method of drug discovery. As biomedical literature has been increasing exponentially, artificial intelligence (AI), especially natural language processing (NLP) offers an efficient way of mining this unstructured data to identify repurposing candidates. The given paper investigates the creation and use of drug repurposing models powered by AI and based on biomedical literature mining. The approach combines deep learning, entity recognition and knowledge graph building to find concealed drug-disease connections in a systematic way. The findings show that the model can be used to correctly forecast new drug indications, some of which have a supported view of available evidence (experimental or clinical) of any kind. This paper illuminates the revolutionary nature of AI to facilitate the process of drug repurposing, by automating knowledge gained in large repositories of text.
Keywords: Drug repurposing, artificial intelligence, biomedical literature mining, NLP, deep learning, knowledge graphs, drug discovery, entity recognition.
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