Traditional and Novel Computer-Aided Drug Design (CADD) Approaches in the Anticancer Drug Discovery Process

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BACKGROUND: In the last decade, cancer has been a leading cause of death worldwide. Despite the impressive progress in cancer therapy, firsthand treatments are not selective to cancer cells and cause serious toxicity. Thus, the design and development of selective and innovative small molecule drugs is of great interest, particularly through in silico tools.

OBJECTIVE: The aim of this review is to analyze different subsections of computer-aided drug design (CADD) in the process of discovering anticancer drugs.

METHODS: Articles from the 2008-2021 timeframe were analyzed and based on the relevance of the information and the JCR of its journal of precedence, were selected to be included in this review.

RESULTS: The information collected in this study highlights the main traditional and novel CADD approaches used in anticancer drug discovery, its sub-segments, and some applied examples. Throughout this review, the potential use of CADD in drug research and discovery, particularly in the field of oncology, is evident due to the many advantages it presents.

CONCLUSION: CADD approaches play a significant role in the drug development process since they allow a better administration of resources with successful results and a promising future market and clinical wise.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:23

Enthalten in:

Current cancer drug targets - 23(2023), 5 vom: 27., Seite 333-345

Sprache:

Englisch

Beteiligte Personen:

Del Carmen Quintal Bojórquez, Nidia [VerfasserIn]
Campos, Maira Rubi Segura [VerfasserIn]

Links:

Volltext

Themen:

Anticancer drugs
Anticancer therapy
Antineoplastic Agents
Artificial intelligence
CADD approaches
Computer-aided drug design
In silico
Journal Article
Novel CADD
Research Support, Non-U.S. Gov't
Review
Traditional CADD

Anmerkungen:

Date Completed 16.03.2023

Date Revised 15.04.2023

published: Print

Citation Status MEDLINE

doi:

10.2174/1568009622666220705104249

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM343127660