Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature

• Type: Another Publication in an International Scientific Journal
• Date: 2023

Authors

Kleandrova, VV (Author) Other; Natalia N D S Cordeiro (Author) FCUP; Speck-Planche, A (Author) Other

Journal

Title: Expert Opinion on Drug Discovery

Vol. 18

Pages: 1231-1243

ISSN: 1746-0441

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-ZVS

DOI: 10.1080/17460441.2023.2251385

Abstract (EN): Introduction: Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations.Areas covered: The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process.Expert opinion: Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13