AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products
Avinash D. Khadela, Sagar Popat, Jinal Ajabiya, Disha Valu, Shrinivas S. Savale, Vivek P. Chavda · 2023
In past few years, the pharma industry has seen a significant expansion in the digitalization of data. However, with digitalization comes the difficulty of acquiring, evaluating, and utilizing information to address complicated clinical situations. Traditional pharmaceutical research can be replaced by artificial intelligence (AI), which consists of a number of sophisticated tools and networks that can simulate the human mind and physiology. AI and machine learning (ML) play a significant role in medicinal development, including the prediction of pharmacological targets and the characteristics of small molecules. For the rapid creation of cellular and genetic therapeutics, AI- and ML-assisted dataset analysis presents a potent and promising route. This relatively young and fast developing discipline that is still in its infancy is evolving at an astounding pace. It is crucial, therefore, to evaluate the creation of new algorithms, techniques, and tools, as well as the challenges, setbacks, and other obstacles emerging during their development, in order to support the growth of this very important sector. In this chapter, we present an overview of existing AI- and ML-based technologies and a peek of how AI and ML is reinventing preclinical and clinical drug research by showcasing real-world applications of AI and ML. In light of the hype and exaggeration surrounding AI and ML in drug development, we hope to give a realistic perspective by examining both the advantages and limitations of using AI and ML in drug research.