**6. Role of artificial intelligence in preformulation studies**

A large, multidisciplinary discipline known as artificial intelligence gives machines the ability to think, learn, and reason. Artificial intelligence has two subsets: machine learning and deep learning. Scientists frequently integrate computer-added drug design tools with artificial intelligence, powered decisionmaking at crucial stages of drug discovery programs [1, 73]. Deep learning-based artificial neural networks and machine learning-based expert systems are currently very well-liked for predicting interactions between drugs and their targets as well as physicochemical properties, quality, stability, toxicity, safety, and biological activity of formulations. Medical diagnostics, epidemic breakouts, and individualised treatments are all examples of how AI is used in the healthcare industry [34]. The healthcare industry pursues exceptional advancements with the help of AI tools for example Adaptive neuro-fuzzy inference system (ANFIS) performance is satisfactory for excipients selection hence the AI-based algorithms made drug research simple and shorten the drug discovery and development timelines. In-silico models found their way as successful tools for determining drugs' aqueous solubilities. These factors include molecular size, molecular shape, and hydrogen bonding capacities [74].
