Determination of disintegration time using formulation data for solid dosage oral formulations via advanced machine learning integrated optimizer models.

We evaluated the properties of tablets by artificial intelligence and machine learning computational approach with integration of optimizer. A large dataset on formulations properties and corresponding tablet disintegration time was collected and the models were used to fit the dataset. Utilizing a dataset of approximately 2,000 entries encompassing molecular properties, physical properties, excipient composition, and formulation characteristics, three ML models were evaluated: TabNet, Radial Basis Function Support Vector Regression (RBF-SVR), and Neural Oblivious Decision Ensembles (NODE). Data preprocessing involved Min-Max normalization, outlier detection via Elliptic Envelope, and feature selection using Conditional Mutual Information, with hyperparameters optimized through the Water Cycle Algorithm. Performance was assessed using R², RMSE, and MAE across train, validation, and test sets, with 95% confidence intervals confirming robust predictions. NODE demonstrated great accuracy for fitting the data, with the highest calculated test R² (0.9805) and the lowest RMSE (7.078) and MAE (5.913), outperforming TabNet (R²: 0.9657, RMSE: 9.382, MAE: 7.299) and RBF-SVR (R²: 0.9652, RMSE: 9.452, MAE: 7.127). These findings highlight NODE's efficacy in modeling complex data relationships, offering significant potential for optimizing tablet formulations in pharmaceutical research to design proper fast-disintegrating tablets.