USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES TO ENHANCE CANCER THERAPY AND DRUG DISCOVERY

Abstract

This paper looks at how AI and machine learning have been applied over the last ten years to the development of anti-cancer drugs. By speeding up the synthesis of more desirable compounds and the identification of new ones, artificial intelligence (AI) has demonstrated substantial contributions to the research and therapy of anti-cancer therapies. Future developments in human cancer research and treatment are anticipated to be significantly influenced by AI. Protein-interaction network analysis, drug target prediction, binding site prediction, and virtual screening are examples of innovative techniques. Drug design and screening are enhanced by machine learning, and the use of multitarget drug development approaches has made it possible to develop cancer treatments with fewer side effects. AI does, however, have several drawbacks, such as a heavy reliance on data and a narrow scope of explanation. Interpretable AI models, which combine data and computation in AI-assisted cancer treatment research, will be the new development path in the future. For more than thirty years, computer-aided drug design techniques have been a key component in the advancement of cancer therapies. Artificial intelligence is a new and powerful technology that has the potential to speed up, lower the cost, and improve the efficacy of anti-cancer therapy development. This work is a narrative review that examines numerous uses of AI-based techniques in the development of anti-cancer medications. We go on to explain the underlying ideas of these techniques as well as their benefits and drawbacks. In addition, we aggregate a multitude of databases, such as the pharmacological, chemical compound, omics, and epigenomics databases. They can be taken into consideration and modified to suit the needs of other researchers.