Research paper on At a Glance: The Emerging Potential of Computational Methods in Cancer Research

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Title: At a Glance: The Emerging Potential of Computational Methods in Cancer Research

Abstract: New medicines that empower the immune system to attack cancer cells have been developed as a result of research into the immune system and cancer. Drugs that target and kill cancer cells specifically are in development, and there are also medications that use particular signals to prevent cancer cells from proliferating. Research on complex diseases can be significantly accelerated by the use of machine learning algorithms, which will aid in the search for new treatments. One field of medical research that machine learning could greatly advance The study of cancer genomes and the identification of the most effective treatment regimens for various disease subtypes are accomplished through algorithms. However, the process of creating a new drug is time-consuming, difficult, risky, and expensive. Production of conventional drugs can take up to 15 years and cost over $1 billion. As a result, computer-aided drug design (CADD) has become a potent and promising technology for creating designs that are quicker, less expensive, and more effective. In order to improve drug development productivity and analytical methodologies, numerous new technologies and methodologies have been developed, and they have now become an essential component of many drug discovery programs. For instance, many scanning programs use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we looked at various computational approaches with an emphasis on anticancer drugs. Still out of reach is machine-based learning in basic and translational cancer research, which could enable new heights of personalized medicine characterized by quick and sophisticated data analysis. To put an end to cancer as we currently know it, we must make sure that every patient has access to reliable treatments. The design of anticancer drugs has been significantly and remarkably impacted by recent advances in computational drug discovery technologies, which have also provided helpful insights into the field of cancer therapy. In this paper, we reviewed the various aspects of computer-aided drug development with a focus on anticancer drugs. Among the bioinformatics tools used to predict anticancer drugs and treatment combos based on multi-omics data are biological networks, functional genomics, toxicogenomics, and transcriptomics. We think that the development of new cancer treatment strategies will benefit from a broad review of the databases that are currently accessible and the computational methods employed today.

Keywords: cancer; immune system; computational tools; computer-based drug design; machine learning algorithms

Paper Quality: SCOPUS / Web of Science Level Research Paper

Subject: Bioengineering

Writer Experience: 20+ Years

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