Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. (2nd September 2022)

• Record Type: Journal Article
• Authors: Veríssimo, Gabriel C.; Serafim, Mateus Sá M.; Kronenberger, Thales; Ferreira, Rafaela S.; Honorio, Kathia M.; Maltarollo, Vinícius G.
• Is Part Of: Expert opinion on drug discovery. Volume 17:Number 9(2022)
• Page Start: 929
• Page End: 947
• Publication Date: 2022-09-02
• Subjects: Drug discovery -- drug design -- machine learning -- one-shot learning -- quantum mechanics -- molecular docking -- molecular dynamics; 615.1
• DOI: 10.1080/17460441.2022.2114451
• Languages: English
• ISSNs: 1746-0441

Abstract

ABSTRACT: Introduction: Modern drug discovery is generally accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. Areas covered: One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. Expert opinion: Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.