Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction-Reference-Cited by-同舟云学术

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Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction

Published:2023-12-05 Issue: Volume:abs/2308.01921 Page:4124-4131
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
language:
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Author:

Shi Wenqi¹,Murakoso Mio²,Guo Xiaoyan³,Wang May D.⁴

Affiliation:

1. Georgia Institute of Technology,Department of Electrical and Computer Engineering,Atlanta,USA

2. Kyoto University,Department of Applied Life Science,Kyoto,Japan

3. Georgia Institute of Technology,College of Computing,Atlanta,USA

4. Georgia Institute of Technology,Wallace H. Coulter Department of Biomedical Engineering,Atlanta,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385756.pdf?arnumber=10385756

Reference44 articles.

1. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods

2. A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease

3. Machine learning for molecular and materials science

4. Deep surrogate docking: Accelerating automated drug discovery with graph neural networks;Hosseini,2022

5. Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

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