Prediction of Potential Repurposed Drugs against SARS-CoV-2 based on Text Mining and Molecular Docking Analysis-Reference-Cited by-同舟云学术

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Prediction of Potential Repurposed Drugs against SARS-CoV-2 based on Text Mining and Molecular Docking Analysis

Published:2023-09-22 Issue: Volume:4 Page:1463-1469
ISSN:
Container-title:2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR)
language:
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Author:

Zhou Jie¹^ORCID,Xu Yi-Qing¹^ORCID

Affiliation:

1. Guangdong Key Lab of Communication and Computer Network,School of Computer Science and Engineering, South China University of Technology, China

Funder

National Natural Science Foundation of China

Innovation Foundation of High-end Scientific Research Institutions in Zhongshan of China

Natural Science Foundation of Guangdong Province of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3641584.3641805

Reference32 articles.

1. Development of variant‐proof severe acute respiratory syndrome coronavirus 2, pan‐sarbecovirus, and pan‐β‐coronavirus vaccines

2. Multidimensional in silico strategy for identification of natural polyphenols-based SARS-CoV-2 main protease (Mpro) inhibitors to unveil a hope against COVID-19

3. Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

4. In silico approaches for drug repurposing in oncology: Protocol for a scoping review of existing evidence

5. Advances in the computational landscape for repurposed drugs against COVID-19

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