Novel machine learning approach toward classification model of HIV-1 integrase inhibitors-Reference-Cited by-同舟云学术

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors

Published:2024 Issue:21 Volume:14 Page:14506-14513
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Phan Tieu-Long¹²^ORCID,Trinh The-Chuong³^ORCID,To Van-Thinh⁴^ORCID,Pham Thanh-An⁴^ORCID,Van Nguyen Phuoc-Chung⁴^ORCID,Phan Tuyet-Minh⁴^ORCID,Truong Tuyen Ngoc⁴^ORCID

Affiliation:

1. Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany

2. Department of Mathematics and Computer Science, University of Southern Denmark, Odense M, DK-5230, Denmark

3. Faculty of Pharmacy, Grenoble Alpes University, La Tronche, 38700, France

4. Falcuty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, 700000, Vietnam

Abstract

HIV-1 (human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D4RA02231A

Reference38 articles.

1. Whole person HIV services: a social science approach

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3. The role of non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the therapy of HIV-1 infection1Presented at the Eleventh International Conference on Antiviral Research, San Diego, CA, 5–10 April 1998.1

4. Analysis of the efficacy of HIV protease inhibitors against SARS-CoV-2′s main protease

5. Strand transfer inhibitors of HIV-1 integrase: Bringing IN a new era of antiretroviral therapy

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