Initial development of web application to support excipient selection for immediate release tablets using message passing neural network

The formulation of immediate release tablets is a challenging task due to the need to balance multiple factors such as bioavailability and stability. The selection of appropriate excipients is critical in achieving these objectives. The recent successful application of Artificial Intelligence (AI) and Message Passing Neural Network (MPNN) in predicting physicochemical and biological properties of drug molecules suggests for the researchers on the ability to apply these models in selecting excipients. The aim of this study is to develop an innovative approach for selecting excipients using AI and MPNN and to create a user-friendly web application to support excipient selection for immediate release tablets. The study utilized a database of 13,278 immediate-release tablets to train, validate, and test the MPNN model on the basis of Simplified Molecular-Input Line-Entry system (SMILES) of drug substances. The performance of the model was validated based on its ability to predict the probability of selecting an excipient reasonably. A web application named FormAI was developed using the Streamlit web framework and integrated with the trained model. The MPNN model demonstrated good performance, with an average Area Under Curve > 0.98 and R2 > 0.99, indicating its ability to predict the probability of selecting an excipient reasonably. The FormAI application provides a user-friendly platform for excipient selection. The results of the study demonstrate the potential of using AI and MPNN in drug formulation design, specifically in excipient selection for immediate-release tablets. The FormAI application provides a practical solution for pharmaceutical scientists and formulators.