Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. (2nd September 2021)

• Record Type: Journal Article
• Authors: Serafim, Mateus Sá Magalhães; dos Santos Júnior, Valtair Severino; Gertrudes, Jadson Castro; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria
• Is Part Of: Expert opinion on drug discovery. Volume 16:Number 9(2021)
• Page Start: 961
• Page End: 975
• Publication Date: 2021-09-02
• Subjects: Machine learning -- antivirals -- drug design -- drug discovery; 615.1
• DOI: 10.1080/17460441.2021.1918098
• Languages: English
• ISSNs: 1746-0441

Abstract

ABSTRACT: Introduction : Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals. Areas covered : The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area. Expert opinion : From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.