Discovering Anti-Cancer Drugs via Computational Methods

Copyright © 2020 Cui, Aouidate, Wang, Yu, Li and Yuan..

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:11

Enthalten in:

Frontiers in pharmacology - 11(2020) vom: 19., Seite 733

Sprache:

Englisch

Beteiligte Personen:

Cui, Wenqiang [VerfasserIn]
Aouidate, Adnane [VerfasserIn]
Wang, Shouguo [VerfasserIn]
Yu, Qiuliyang [VerfasserIn]
Li, Yanhua [VerfasserIn]
Yuan, Shuguang [VerfasserIn]

Links:

Volltext

Themen:

AI
Anti-cancer
CADD
Computational methods
Drug discovery
Journal Article
Review

Anmerkungen:

Date Revised 28.03.2024

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.3389/fphar.2020.00733

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM310881471