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

• URL: http://arxiv.org/abs/2202.05145v1
• Date: Tue, 8 Feb 2022 09:42:08 GMT
• Title: Deep learning for drug repurposing: methods, databases, and applications
• Authors: Xiaoqin Pan, Xuan Lin, Dongsheng Cao, Xiangxiang Zeng, Philip S. Yu, Lifang He, Ruth Nussinov, Feixiong Cheng
• Abstract summary: Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
• Score: 54.08583498324774
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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