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

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.

Automated summary

Drug discovery is crucial in fighting against various diseases, and computational methods have played an important role in rationalizing the search for novel drugs. However, the current computational methods have limitations in tackling multi-genic diseases and drug resistance. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome these limitations.