Semantic Review of Artificial Intelligence Architectures in Drug Discovery

Within the pharmaceutical sector, the implementation of Artificial Intelligence (AI) in the process of drug discovery and development is being heralded as a paradigm-shifting development. Traditional approaches to drug discovery have faced numerous limitations. This paper seeks to explore the innovative ways in which AI is transforming the field of drug discovery. The first section of the paper will delve into the constraints of traditional drug discovery methods, including the challenges of analyzing large data sets and developing effective drugs within a reasonable timeframe. The second part of the paper will explore the application of AI in pharmacology, with a particular focus on the different types of AI architectures employed. The third section of the paper will undertake a comparative analysis of the various AI architectures, examining their respective advantages and disadvantages. This analysis will provide an in-depth understanding of the strengths and weaknesses of each approach. In conclusion, the paper will draw on the insights gleaned from the preceding sections to highlight the transformative impact of AI on drug discovery and development. It will emphasize the importance of embracing AI as a key tool in the pursuit of more efficient and effective drug development.