In recent years, artificial intelligence (AI) has developed into a potent instrument that taps into the expertise of humans in order to swiftly address difficult problems. Using artificial intelligence algorithms that analyse enormous volumes of biological data, such as proteomics and genomics, researchers are able to discover targets that are related with disease and forecast potential interactions between those targets and therapy alternatives. This makes it possible to conduct pharmacological research in a manner that is both more efficient and more targeted, which in turn raises the possibility that medications will be approved. Through the optimisation of research and development procedures, artificial intelligence can also help reduce the expenses of development. The capacity to prioritise and optimise lead compounds is made feasible by this skill, which in turn decreases the requirement for animal research, which is both time-consuming and expensive. AI algorithms that evaluate real-world patient data have the potential to improve personalised medicine procedures, which in turn can lead to improved treatment outcomes and increased clinical adherence amongst patients. In addition, artificial intelligence has the potential to streamline research and development processes, which can assist reduce development costs. Machine learning algorithms, which also assist with the design of experiments, can be used to provide predictions about the pharmacokinetics and toxicity of proposed medications. This article provides a comprehensive review of the various AI-based strategies that are utilised in pharmaceutical technology. It highlights both the benefits of these techniques as well as the fact that they are innovative and forward-thinking.
Shukla et al. (2025) studied this question.