Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds-Reference-Cited by-同舟云学术

Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds

Published:2023-11-07 Issue:46 Volume:8 Page:43813-43826
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

van Heerden Ashleigh¹,Turon Gemma²,Duran-Frigola Miquel²^ORCID,Pillay Nelishia³,Birkholtz Lyn-Marié¹^ORCID

Affiliation:

1. Department of Biochemistry, Genetics and Microbiology, Institute for Sustainable Malaria Control, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

2. Ersilia Open Source Initiative, 28 Belgrave Road, Cambridge CB1 3DE, U.K.

3. Department of Computer Science, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

Funder

National Research Foundation

Department of Science and Innovation, South Africa

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c05664

Reference59 articles.

1. World Health Organization. World Malaria Report 2020:20 Years of Global Progress and Challenges; World Health Organization Geneva, 2020; pp 1–151.

2. Potential Emergence of Plasmodium Resistance to Artemisinin Induced by the Use of Artemisia annua for Malaria and COVID-19 Prevention in Sub-African Region

3. Multistage and transmission-blocking targeted antimalarials discovered from the open-source MMV Pandemic Response Box

4. MalDA, Accelerating Malaria Drug Discovery

5. Transmission-blocking drugs for malaria elimination

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