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Expert Opinion on Drug Discovery Volume 16, 2021 - Issue 9: Artificial Intelligence (AI) in Drug Discovery
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Review

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Mateus Sá Magalhães Serafima Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, BrazilCorrespondence[email protected]
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,
Valtair Severino dos Santos Júniorb Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, BrazilCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4291-8624View further author information
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Jadson Castro Gertrudesc Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, BrazilView further author information
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Vinícius Gonçalves Maltarollob Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, BrazilView further author information
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Kathia Maria Honoriod Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil;e Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, BrazilCorrespondence[email protected]
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Pages 961-975 | Received 26 Feb 2021, Accepted 13 Apr 2021, Published online: 07 May 2021

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Gabriel C. Veríssimo, Mateus Sá M. Serafim, Thales Kronenberger, Rafaela S. Ferreira, Kathia M. Honorio & Vinícius G. Maltarollo. (2022) Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opinion on Drug Discovery 17:9, pages 929-947.
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Articles from other publishers (8)

Mateus Sá Magalhães Serafim, Thales Kronenberger, Renata Barbosa de Oliveira, Erna Geessien Kroon, Jônatas Santos Abrahão, Bruno Eduardo Fernandes Mota & Vinícius Gonçalves Maltarollo. (2023) Synthetic Curcumin Analogues Present Antiflavivirus Activity In Vitro with Potential Multiflavivirus Activity from a Thiazolylhydrazone Moiety. Future Pharmacology 3:2, pages 364-378.
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Rashmi Kumari, Suresh D. Sharma, Amrita Kumar, Zachary Ende, Margarita Mishina, Yuanyuan Wang, Zackary Falls, Ram Samudrala, Jan Pohl, Paul R. Knight & Suryaprakash Sambhara. (2023) Antiviral Approaches against Influenza Virus. Clinical Microbiology Reviews 36:1.
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Gabriel Corrêa Veríssimo, Valtair Severino dos Santos Junior, Philipe Oliveira Fernandes, Shoichi Ishida, Ryosuke Kojima, Yasushi Okuno, Jadson Castro Gertrudes & Vinicius Gonçalves Maltarollo. (2022) GCN-Based Structure-Activity Relationship and DFT Studies of Staphylococcus aureus FabI Inhibitors. International Journal of Quantitative Structure-Property Relationships 7:1, pages 1-16.
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Md. Mominur Rahman, Md. Rezaul Islam, Firoza Rahman, Md. Saidur Rahaman, Md. Shajib Khan, Sayedul Abrar, Tanmay Kumar Ray, Mohammad Borhan Uddin, Most. Sumaiya Khatun Kali, Kamal Dua, Mohammad Amjad Kamal & Dinesh Kumar Chellappan. (2022) Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance. Bioengineering 9:8, pages 335.
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Sethu Arun Kumar, Thirumoorthy Durai Ananda Kumar, Narasimha M Beeraka, Gurubasavaraj Veeranna Pujar, Manisha Singh, Handattu Sankara Narayana Akshatha & Meduri Bhagyalalitha. (2022) Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Medicinal Chemistry 14:4, pages 245-270.
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Jinxian Wang, Ying Zhang, Wenjuan Nie, Yi Luo & Lei Deng. (2022) Computational anti-COVID-19 drug design: progress and challenges. Briefings in Bioinformatics 23:1.
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Qiaoming Liu, Jun Wan & Guohua Wang. (2022) A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Briefings in Bioinformatics 23:1.
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Philipe Oliveira Fernandes, Diego Magno Martins, Aline de Souza Bozzi, João Paulo A. Martins, Adolfo Henrique de Moraes & Vinícius Gonçalves Maltarollo. (2021) Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking. Molecular Diversity 25:3, pages 1301-1314.
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