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

ABSTRACT

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.

KEYWORDS:

• mtk-QSBER
• QSAR
• topological indices
• fragment-based topological design
• PTML
• Box-Jenkins approach

Article highlights

• Current computational methods have limitations that prevent them from solving the current challenges in drug discovery

• Mtk-QSBER models can overcome all the limitations of modern computational methods for drug discovery.

• The Box-Jenkins approach is the core step for the development of mtk-QSBER models.

• Mtk-QSBER models can accelerate drug development in a multi-target drug discovery scenario.

• Mtk-QSBER models offer encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This work was financially supported by the Foundation for Science and Technology/the Ministry of Science, Technology and Higher Education of the Government of Portugal, through grant UIDB/50006/2020.