Computational Methods and Tools for Repurposing of Drugs Against Coronaviruses

The conventional drug discovery pipeline involving discovery of a new molecule requires huge investment and long time. Therefore, re-using the existing drugs for new indication which is termed as “drug repurposing” is of growing importance to the scientific community. The current pandemic has furthe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Chakraborti, Sohini, Bheemireddy, Sneha, Srinivasan, Narayanaswamy
Format: Buchkapitel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The conventional drug discovery pipeline involving discovery of a new molecule requires huge investment and long time. Therefore, re-using the existing drugs for new indication which is termed as “drug repurposing” is of growing importance to the scientific community. The current pandemic has further reinforced the importance of drug repurposing especially when the world is looking forward to a quick and effective solution to treat Covid19. Computational approaches play a crucial role in hastening the overall drug repurposing pipeline. In this chapter, we discuss how the knowledge on protein sequence, structure, and dynamics can be exploited to search for potential anti-Covid19 molecules from the repertoire of approved/under-trial drugs by using various computational tools and resources. Over the past few years, our group has been involved in repurposing drugs against various diseases including Covid19. The learning from our experiences which could improve the reliability on the computational predictions has also been discussed. Overall, this chapter provides a bird's-eye view of various computational methods which can be efficiently used to repurpose drugs against any disease. We hope especially the non-experts in the field could be benefitted from our learning we are sharing in this chapter.
ISSN:1557-2153
1940-6053
DOI:10.1007/7653_2020_60