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.