Machine Learning-Driven Formulation and Process Design for Solid Dosage Forms

Formulating robust solid dosage forms remains one of the most complex challenges in pharmaceutical development. Building on the foundation of the Informex data management software, we developed a machine learning–augmented expert system that recommends optimal tablet formulations and manufacturing methods based on the known physicochemical and mechanical properties of the drug substance. This AI-driven platform assists scientists in both de novo formulation and iterative improvement of existing products. Key features include prediction of excipient selection, API-to-excipient ratios, granulation and compression parameters, and manufacturing pathway (e.g., direct compression, wet granulation, or dry granulation). Integrated models—such as Random Forest, Support Vector Machines, and ANN—enable performance optimization through learning from historical formulation data. Informex empowers formulators with predictive decision-support tools, shortens development timelines, and enhances product robustness. This system represents a step toward intelligent, data-driven pharmaceutical development aligned with modern QbD and digital transformation principles.