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

Rahman, MM; Islam, MR; Rahman, F; Rahaman, MS; Khan, MS; Abrar, S; Ray, TK; Uddin, MB; Kali, MSK; Dua, K; Kamal, MA; Chellappan, DK

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

Rahman, MM Islam, MR Rahman, F Rahaman, MS Khan, MS Abrar, S Ray, TK Uddin, MB Kali, MSK Dua, K

Kamal, MA Chellappan, DK

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Bioengineering, 9, (8), pp. 335-335

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Field	Value	Language
dc.contributor.author	Rahman, MM
dc.contributor.author	Islam, MR
dc.contributor.author	Rahman, F
dc.contributor.author	Rahaman, MS
dc.contributor.author	Khan, MS
dc.contributor.author	Abrar, S
dc.contributor.author	Ray, TK
dc.contributor.author	Uddin, MB
dc.contributor.author	Kali, MSK
dc.contributor.author	Dua, K https://orcid.org/0000-0002-7507-1159
dc.contributor.author	Kamal, MA
dc.contributor.author	Chellappan, DK
dc.date.accessioned	2022-07-25T12:15:02Z
dc.date.available	2022-07-25T12:15:02Z
dc.identifier.citation	Bioengineering, 9, (8), pp. 335-335
dc.identifier.issn	2306-5354
dc.identifier.uri	http://hdl.handle.net/10453/159157
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Bioengineering
dc.relation.isbasedon	10.3390/bioengineering9080335
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance
dc.type	Journal Article
utslib.citation.volume	9
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Graduate School of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Graduate School of Health/GSH.Pharmacy
utslib.copyright.status	open_access	*
dc.date.updated	2022-07-25T12:15:01Z
pubs.issue	8
pubs.publication-status	Published online
pubs.volume	9
utslib.citation.issue	8

Abstract:

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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