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Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine lea...

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Autores principales: Rahman, Md. Mominur, Islam, Md. Rezaul, Rahman, Firoza, Rahaman, Md. Saidur, Khan, Md. Shajib, Abrar, Sayedul, Ray, Tanmay Kumar, Uddin, Mohammad Borhan, Kali, Most. Sumaiya Khatun, Dua, Kamal, Kamal, Mohammad Amjad, Chellappan, Dinesh Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332125/
https://www.ncbi.nlm.nih.gov/pubmed/35892749
http://dx.doi.org/10.3390/bioengineering9080335
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author Rahman, Md. Mominur
Islam, Md. Rezaul
Rahman, Firoza
Rahaman, Md. Saidur
Khan, Md. Shajib
Abrar, Sayedul
Ray, Tanmay Kumar
Uddin, Mohammad Borhan
Kali, Most. Sumaiya Khatun
Dua, Kamal
Kamal, Mohammad Amjad
Chellappan, Dinesh Kumar
author_facet Rahman, Md. Mominur
Islam, Md. Rezaul
Rahman, Firoza
Rahaman, Md. Saidur
Khan, Md. Shajib
Abrar, Sayedul
Ray, Tanmay Kumar
Uddin, Mohammad Borhan
Kali, Most. Sumaiya Khatun
Dua, Kamal
Kamal, Mohammad Amjad
Chellappan, Dinesh Kumar
author_sort Rahman, Md. Mominur
collection PubMed
description Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
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spelling pubmed-93321252022-07-29 Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance Rahman, Md. Mominur Islam, Md. Rezaul Rahman, Firoza Rahaman, Md. Saidur Khan, Md. Shajib Abrar, Sayedul Ray, Tanmay Kumar Uddin, Mohammad Borhan Kali, Most. Sumaiya Khatun Dua, Kamal Kamal, Mohammad Amjad Chellappan, Dinesh Kumar Bioengineering (Basel) Review Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches. MDPI 2022-07-25 /pmc/articles/PMC9332125/ /pubmed/35892749 http://dx.doi.org/10.3390/bioengineering9080335 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Rahman, Md. Mominur
Islam, Md. Rezaul
Rahman, Firoza
Rahaman, Md. Saidur
Khan, Md. Shajib
Abrar, Sayedul
Ray, Tanmay Kumar
Uddin, Mohammad Borhan
Kali, Most. Sumaiya Khatun
Dua, Kamal
Kamal, Mohammad Amjad
Chellappan, Dinesh Kumar
Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
title Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
title_full Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
title_fullStr Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
title_full_unstemmed Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
title_short Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
title_sort emerging promise of computational techniques in anti-cancer research: at a glance
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332125/
https://www.ncbi.nlm.nih.gov/pubmed/35892749
http://dx.doi.org/10.3390/bioengineering9080335
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