An up-to-date overview of computational polypharmacology in modern drug discovery

ABSTRACT

Introduction

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

Areas covered

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

Expert opinion

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

KEYWORDS:

• Drug Polypharmacology
• multi-targeting Design
• drug Repurposing
• artificial Intelligence
• deep Learning
• multi-omics
• network Pharmacology
• molecular Promiscuity
• off-targets

Article highlights

• This review article covers the updated computational aspects of polypharmacology, focusing on reports published after 2016, including the efforts addressing the current COVID-19 pandemic.

• Many new technologies and transformative concepts have been developed for multi-targeting agents with clinical successes.

• High-level data curation/integration and novel computational approaches have been developed, but more are needed for robust and accurate polypharmacology prediction.

• More experimental assays and models are in great demand to study the multi-targeting and multi-functional effects of compounds for different diseases.

• Deep learning and AI technologies, in combination with multi-omics big data, are being adopted for retrieving hidden and complex relations between biological targets and chemical entities.

• Significant challenges still lie ahead for polypharmacology studies, in particular for rational design of effective multi-targeting agents.

This box summarizes key points contained in the article.

Acknowledgments

The authors give special thanks to the HPC resources from Texas Advanced Computing Center (TACC) and the University of Texas M.D. Anderson Cancer Center.

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.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

S Zhang is partially supported by the Institutional Research Grant (IRG) Program at The University of Texas MD Anderson Cancer Center, CPRIT RP170333, and NIH/NCI grants 1R01CA225955-01 and P30CA016672.