FormulationDE: an updated artificial intelligence system for drug-excipient compatibility prediction

Pharmaceutical formulations comprise active pharmaceutical ingredients (APIs) and excipients, the latter of which can significantly influence drug stability. Compatibility studies between drugs and excipients are essential during the pre-formulation stage. To address the challenges posed by time-consuming and costly compatibility experiments, this study enhanced the PharmDE expert system into a machine learning-based AI platform for compatibility prediction. In this work, we first established a comprehensive database containing 1,105 entries of compatibility data, including 579 compatible and 526 incompatible data. Subsequently, a machine learning model was trained using this database, achieving performance metrics with an accuracy of 0.75, precision of 0.75, recall of 0.75, F1 score of 0.74, MCC of 0.50, and AUC of 0.82. Finally, the machine learning model was integrated with the expert system to create FormulationDE—an interpretable platform for predicting drug-excipient compatibility. This platform not only facilitates rapid predictions regarding drug-excipient compatibility but also provides risk assessments for potential interacting groups based on SHAP force plots that highlight high-contribution functional groups. We believe that FormulationDE has potential as a valuable tool in the pharmaceutical industry by reducing both time and costs associated with experimental studies in drug development.