Your browser doesn't support javascript.
A computational approach to drug repurposing using graph neural networks.
Doshi, Siddhant; Chepuri, Sundeep Prabhakar.
  • Doshi S; Indian Institute of Science, Bangalore, 560012, India. Electronic address: siddhant.doshi@outlook.com.
  • Chepuri SP; Indian Institute of Science, Bangalore, 560012, India. Electronic address: spchepuri@iisc.ac.in.
Comput Biol Med ; 150: 105992, 2022 Aug 31.
Article in English | MEDLINE | ID: covidwho-2003986
ABSTRACT
Drug repurposing is an approach to identify new medical indications of approved drugs. This work presents a graph neural network drug repurposing model, which we refer to as GDRnet, to efficiently screen a large database of approved drugs and predict the possible treatment for novel diseases. We pose drug repurposing as a link prediction problem in a multi-layered heterogeneous network with about 1.4 million edges capturing complex interactions between nearly 42,000 nodes representing drugs, diseases, genes, and human anatomies. GDRnet has an encoder-decoder architecture, which is trained in an end-to-end manner to generate scores for drug-disease pairs under test. We demonstrate the efficacy of the proposed model on real datasets as compared to other state-of-the-art baseline methods. For a majority of the diseases, GDRnet ranks the actual treatment drug in the top 15. Furthermore, we apply GDRnet on a coronavirus disease (COVID-19) dataset and show that many drugs from the predicted list are being studied for their efficacy against the disease.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article