TYPE Original Research PUBLISHED 06 March 2023 DOI 10.3389/fpubh.2023.1125917 OPEN ACCESS EDITED BY Reza Lashgari, Shahid Beheshti University, Iran REVIEWED BY Dezso Modos, Quadram Institute, United Kingdom Yash Gupta, Mayo Clinic Florida, United States *CORRESPONDENCE Tanvir Alam talam@hbku.edu.qa SPECIALTY SECTION This article was submitted to Infectious Diseases: Epidemiology and Prevention, a section of the journal Frontiers in Public Health RECEIVED 16 December 2022 ACCEPTED 07 February 2023 PUBLISHED 06 March 2023 CITATION Basit SA, Qureshi R, Musleh S, Guler R, Rahman MS, Biswas KH and Alam T (2023) COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19. Front. Public Health 11:1125917. doi: 10.3389/fpubh.2023.1125917 COPYRIGHT © 2023 Basit, Qureshi, Musleh, Guler, Rahman, Biswas and Alam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19 Syed Abdullah Basit 1 , Rizwan Qureshi 1 , Saleh Musleh 1 , Reto Guler 2,3,4 , M. Sohel Rahman 5 , Kabir H. Biswas 6 and Tanvir Alam 1 * 1 College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar, 2 International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, University of Cape Town, Cape Town, South Africa, 3 Department of Pathology, Division of Immunology and South African Medical Research Council (SAMRC) Immunology of Infectious Diseases, Institute of Infectious Diseases and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 4 Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 5 Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, 6 College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID- 19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0. KEYWORDS SARS-CoV-2, CORD-19, deep learning, machine learning, COVID-19 Frontiers in Public Health 01 frontiersin.org