The traditional drug discovery process is expensive, time-consuming, and often leads to a high failure rate. The development of numerous new medications in the pharmaceutical sciences is only one example of how the advancement of artificial intelligence has opened up exciting new opportunities for developing intelligent modelling. Machine learning and deep learning are two examples of artificial intelligence that can sift through large datasets in search of promising new drugs. AI algorithms can predict the binding affinity of molecules to specific targets, helping researchers narrow down the pool of potential drug candidates. Pharmacokinetics and pharmacodynamic are essential aspects of drug development. Drug formulation development requires extensive testing and optimization of various parameters. AI models can quickly analyze data from multiple experiments and identify the most promising formulations, saving time and resources. New pharmaceuticals may be developed and brought to market at a reduced cost and in a shorter amount of time with the use of AI-based optimisation approaches. Absorption, Distribution, Metabolism, and Excretion (ADME) are only some of the aspects of pharmacological physiology that may be modelled and predicted with the use of artificial intelligence. By integrating AI models into the drug development process, researchers can gain a deeper understanding of a drug’s pharmacokinetic and pharmacodynamic properties. This knowledge helps in designing drugs with improved efficacy and reduced side effects. So, in present topic authors tried to give insights how AI is playing a transformative role in pharmaceutical sciences. As AI technology continues to advance, the future of pharmaceutical sciences looks brighter than ever.

Keywords: Machine learning, Artificial intelligence, Drug discovery, Target identification, Artificial Neural Network (ANN)