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Review
. 2020 Sep;15(9):1025-1044.
doi: 10.1080/17460441.2020.1767063. Epub 2020 May 26.

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

Rajan Chaudhari  1 , Long Wolf Fong  1   2 , Zhi Tan  1 , Beibei Huang  1 , Shuxing Zhang  1   2
Affiliations
Review

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

Rajan Chaudhari et al. Expert Opin Drug Discov. 2020 Sep.

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; artificial Intelligence; deep Learning; drug Repurposing; molecular Promiscuity; multi-omics; multi-targeting Design; network Pharmacology; off-targets.

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Conflict of interest statement

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.

Figures

Figure. 1.
Figure. 1.. Current and novel concepts of polypharmacology and multi-targeting agents.
Note: more details for each concept are described in the main text.
Figure. 2.
Figure. 2.
Generalized workflow of AI in polypharmacology predictions.
Figure. 3.
Figure. 3.. Poziotinib repurposing to treat EGFR exon 20 insertion patients.
The insertion increases the rigidity of the α-helix of EGFR, leading to a smaller and more rigid binding pocket. Poziotinib could fit into the pocket and forms covalent bond with Cys800. Magenta: EGFR with T790M; Cyan: EGFR with Exon 20; Green: Poziotinib; Yellow: Cys 800; Red: EGFR SVD insertion.
See this image and copyright information in PMC

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