TY - JOUR
T1 - An up-to-date overview of computational polypharmacology in modern drug discovery
AU - Chaudhari, Rajan
AU - Fong, Long Wolf
AU - Tan, Zhi
AU - Huang, Beibei
AU - Zhang, Shuxing
N1 - Funding Information:
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. 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.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - Drug Polypharmacology
KW - artificial Intelligence
KW - deep Learning
KW - drug Repurposing
KW - molecular Promiscuity
KW - multi-omics
KW - multi-targeting Design
KW - network Pharmacology
KW - off-targets
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U2 - 10.1080/17460441.2020.1767063
DO - 10.1080/17460441.2020.1767063
M3 - Review article
C2 - 32452701
AN - SCOPUS:85085997309
SN - 1746-0441
VL - 15
SP - 1025
EP - 1044
JO - Expert Opinion on Drug Discovery
JF - Expert Opinion on Drug Discovery
IS - 9
ER -