Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery-Reference-Cited by-同舟云学术

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Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery

Published:2024-07-16 Issue:15 Volume:64 Page:5922-5930
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Dorsey Matthew A.¹,Dsouza Kelvin²,Ranganath Dhruv³,Harris Joshua S.⁴,Lane Thomas R.⁴^ORCID,Urbina Fabio⁴,Ekins Sean⁴^ORCID

Affiliation:

1. Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States

2. Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States

3. Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States

4. Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States

Funder

National Institute of Environmental Health Sciences

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00953

Reference44 articles.

1. Exploiting machine learning for end-to-end drug discovery and development

2. Machine Learning Models for Mycobacterium tuberculosis In Vitro Activity: Prediction and Target Visualization

3. Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

4. Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery

5. Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening

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