Prediction of Preformulation Using AI and Machine Learing Approches

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into preformulation studies is transforming the way pharmaceutical research is carried out. This process speeds up the transition from drug discovery to formulation. Preformulation is the first step in drug development. It involves a thorough examination of physicochemical characteristics. This review highlights the first-in-class applications of AI and ML in addressing key challenges, such as predicting solubility, determining pKa values, assessing partition coefficients, testing polymorphism, ensuring stability, and checking drug-excipient compatibility. Neural networks, support vector machines, and gradient boosting models are examples of advanced algorithms that improve the accuracy of predictive models. These tools help accelerate formulation design and lower risks. As much as there are challenges involving data limitations and regulatory regulations, the future of AI and ML in the pharmaceutics industry is significant, promoting innovation, minimizing costs, and maximizing therapeutic potency. This summary intends to give a summary overview of the approaches, achievements, and novel trends in AI/ML applications for pharmaceutical formulation designing.