Drug Discovery Involving Artificial Intelligence and Big Data Modeling: Review

Abstract

Modern drug discovery has progressed to the big data age as a result of the huge data sets accessible for medication candidates. The development of artificial intelligence (AI) techniques to apply creative modeling based on the dynamic, diverse, and enormous character of pharmacological data sets is at the heart of this transition. As a result, recently established AI methodologies such as deep learning and relevant modeling studies provide new ways to drug candidate efficacy and safety evaluations based on large data modeling and analysis. The models that resulted gave researchers a lot of information about the whole process, from chemical structure through in vitro, in vivo, and clinical results. Recent modeling research has benefited greatly from the use of innovative data mining, duration, and management strategies. In conclusion, advances in AI in the big data age have prepared the way for future rational medication development and optimization, which will have a substantial influence on drug discovery methods and, eventually, public health.