From Data to Drugs: Harnessing Machine Learning's Potential for Drug Discovery

Abstract

The field of drug discovery has recently seen the emergence of strong tools in the form of machine learning algorithms, which have the potential to transform the entire process of drug development. This study presents a detailed overview of the applications of machine learning in drug development, with a particular emphasis on target identification, drug efficacy prediction, and drug design optimization. This overview sheds light on the most recent developments, problems, and opportunities available in this dynamic and ever-evolving industry. It gives researchers the ability to prioritize and optimize possible medication ideas, which in turn helps them save money and improve their chances of being successful. Further, machine learning has historically been accomplished by a combination of experimental methods and expert knowledge, which can be time-consuming, pricey, and restricted by the level of biological understanding that is currently available. They provide a variety of learning strategies, both supervised and unsupervised, to examine complicated biological data and anticipate possible targets. Even if there are obstacles and constraints, there is significant progress being made in the accessibility of data, its interpretability, and its connection with other fields, which holds a lot of promise for future target identification initiatives. The harnessing of the power of machine learning will surely, in the future, contribute to the acceleration of the discovery of effective treatments as well as the improvement of patient outcomes.