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Emerging Promise of Computational Techniques in Anti-Cancer Research

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dc.contributor.author Rahman, Md. Mominur
dc.contributor.author Islam, Md. Rezaul
dc.contributor.author Rahman, Firoza
dc.contributor.author Rahaman, Md. Saidur
dc.contributor.author Khan, Md. Shajib
dc.contributor.author Abrar, Sayedul
dc.contributor.author Ray, Tanmay Kumar
dc.contributor.author Uddin, Mohammad Borhan
dc.contributor.author Kali, Most. Sumaiya Khatun
dc.contributor.author Dua, Kamal
dc.contributor.author Kamal, Mohammad Amjad
dc.contributor.author Chellappan, Dinesh Kumar
dc.date.accessioned 2023-12-10T05:11:09Z
dc.date.available 2023-12-10T05:11:09Z
dc.date.issued 2022-07-25
dc.identifier.issn 2306-5354
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11284
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Technology en_US
dc.subject Networking en_US
dc.subject Diseases en_US
dc.subject Cancer en_US
dc.subject Treatment process en_US
dc.title Emerging Promise of Computational Techniques in Anti-Cancer Research en_US
dc.title.alternative At a Glance en_US
dc.type Article en_US


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