Artificial Intelligence and Machine Learning in Drug Discovery and Development

Drug discovery has undergone significant changes over the past decade thanks to artificial intelligence (AI). To improve the efficiency and precision of medication research and development, this research investigates how artificial intelligence (AI) and AI technologies are used in the pharmaceutical industry. An assessment of previous studies has been conducted in this study methodically. In addition to the authors' existing knowledge, openly accessible databases were used to locate these studies. These databases were filtered by context, abstracts, and techniques relevant to the entire text of the study. We will examine two crucial aspects of drug design in this study: structure-based drug design (SBDD) and ligand-based drug design (LBDD). The two aspects are abbreviated "S" and "L" (LBDD). It focuses a considerable amount of its attention on how artificial intelligence tools (AI) can be used to simplify drug discovery and development processes, such as machine learning and deep learning. This makes these procedures more cost-effective, and may eliminate the need for clinical trials as well. Aside from semi-supervised learning, unsupervised learning, and supervised learning, machine learning consists of three subfields. Researchers have found that AI can be applied to many aspects of healthcare, leading to the implementation of AI-based functions. It is important to search for new medicines themselves because it is the most challenging and important part of developing new medicines. Additionally, we examined how AI has improved drug development over the last few years in a substantial way. Using these discoveries, researchers, academics, and the pharmaceutical industry can further explore machine learning, artificial intelligence, and deep learning in the context of drug discovery and development.