An up-to-date overview of computational polypharmacology in modern drug discovery. (1st September 2020)
- Record Type:
- Journal Article
- Title:
- An up-to-date overview of computational polypharmacology in modern drug discovery. (1st September 2020)
- Main Title:
- An up-to-date overview of computational polypharmacology in modern drug discovery
- Authors:
- Chaudhari, Rajan
Fong, Long Wolf
Tan, Zhi
Huang, Beibei
Zhang, Shuxing - Abstract:
- 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 polypharmacologyABSTRACT: 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. … (more)
- Is Part Of:
- Expert opinion on drug discovery. Volume 15:Number 9(2020)
- Journal:
- Expert opinion on drug discovery
- Issue:
- Volume 15:Number 9(2020)
- Issue Display:
- Volume 15, Issue 9 (2020)
- Year:
- 2020
- Volume:
- 15
- Issue:
- 9
- Issue Sort Value:
- 2020-0015-0009-0000
- Page Start:
- 1025
- Page End:
- 1044
- Publication Date:
- 2020-09-01
- Subjects:
- Drug Polypharmacology -- multi-targeting Design -- drug Repurposing -- artificial Intelligence -- deep Learning -- multi-omics -- network Pharmacology -- molecular Promiscuity -- off-targets
615.1 - Journal URLs:
- http://informahealthcare.com/journal/edc ↗
http://informahealthcare.com ↗
http://www.expertopin.com/loi/edc ↗ - DOI:
- 10.1080/17460441.2020.1767063 ↗
- Languages:
- English
- ISSNs:
- 1746-0441
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.002942
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23448.xml