An up-to-date overview of computational polypharmacology in modern drug discovery.
Expert Opin Drug Discov
; 15(9): 1025-1044, 2020 09.
Article
in English
| MEDLINE | ID: covidwho-378198
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Drug Discovery
/
Polypharmacology
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Expert Opin Drug Discov
Year:
2020
Document Type:
Article
Affiliation country:
17460441.2020.1767063
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