Deep learning for drug repurposing: Methods, databases, and applications

Xiaoqin Pan, Xuan Lin*, Dongsheng Cao, Xiangxiang Zeng*, Philip S. Yu, Lifang He, Ruth Nussinov, Feixiong Cheng

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic and outline its future challenges. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning.

Original languageEnglish
Article numbere1597
JournalWiley Interdisciplinary Reviews: Computational Molecular Science
Issue number4
StatePublished - 1 Jul 2022


  • bioinformatics
  • deep learning
  • drug repurposing


Dive into the research topics of 'Deep learning for drug repurposing: Methods, databases, and applications'. Together they form a unique fingerprint.

Cite this