Cargando…
Application of computational methods for anticancer drug discovery, design, and optimization
Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with th...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hospital Infantil de México Federico Gómez. Published by Masson Doyma México S.A.
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7110968/ https://www.ncbi.nlm.nih.gov/pubmed/29421286 http://dx.doi.org/10.1016/j.bmhimx.2016.10.006 |
_version_ | 1783513175171268608 |
---|---|
author | Prada-Gracia, Diego Huerta-Yépez, Sara Moreno-Vargas, Liliana M. |
author_facet | Prada-Gracia, Diego Huerta-Yépez, Sara Moreno-Vargas, Liliana M. |
author_sort | Prada-Gracia, Diego |
collection | PubMed |
description | Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs. |
format | Online Article Text |
id | pubmed-7110968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hospital Infantil de México Federico Gómez. Published by Masson Doyma México S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71109682020-04-02 Application of computational methods for anticancer drug discovery, design, and optimization Prada-Gracia, Diego Huerta-Yépez, Sara Moreno-Vargas, Liliana M. Bol Med Hosp Infant Mex Article Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs. Hospital Infantil de México Federico Gómez. Published by Masson Doyma México S.A. 2016 2016-11-30 /pmc/articles/PMC7110968/ /pubmed/29421286 http://dx.doi.org/10.1016/j.bmhimx.2016.10.006 Text en © 2016 Hospital Infantil de México Federico Gómez. Published by Masson Doyma México S.A. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Prada-Gracia, Diego Huerta-Yépez, Sara Moreno-Vargas, Liliana M. Application of computational methods for anticancer drug discovery, design, and optimization |
title | Application of computational methods for anticancer drug discovery, design, and optimization |
title_full | Application of computational methods for anticancer drug discovery, design, and optimization |
title_fullStr | Application of computational methods for anticancer drug discovery, design, and optimization |
title_full_unstemmed | Application of computational methods for anticancer drug discovery, design, and optimization |
title_short | Application of computational methods for anticancer drug discovery, design, and optimization |
title_sort | application of computational methods for anticancer drug discovery, design, and optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7110968/ https://www.ncbi.nlm.nih.gov/pubmed/29421286 http://dx.doi.org/10.1016/j.bmhimx.2016.10.006 |
work_keys_str_mv | AT pradagraciadiego applicationofcomputationalmethodsforanticancerdrugdiscoverydesignandoptimization AT huertayepezsara applicationofcomputationalmethodsforanticancerdrugdiscoverydesignandoptimization AT morenovargaslilianam applicationofcomputationalmethodsforanticancerdrugdiscoverydesignandoptimization |