AI-Guided Selection of Pharmaceutical Excipients

Abstract

The selection of pharmaceutical excipients remains a knowledge-intensive process that relies heavily on empirical expertise and iterative laboratory experimentation. Although artificial intelligence (AI) has demonstrated significant success in drug discovery and formulation optimization, its application to excipient recommendation remains fragmented and largely focused on isolated formulation variables. This study addresses the research gap by proposing an AI-guided framework that predicts excipient compatibility through multiattribute formulation learning rather than simple ingredient matching. The framework integrates physicochemical properties of active pharmaceutical ingredients (APIs), formulation objectives, and excipient functional characteristics into a unified machine learning pipeline. Unlike traditional decision-support systems, the proposed approach emphasizes explainable predictions to improve formulation transparency. The manuscript discusses recent advances in AI-enabled pharmaceutical development, identifies limitations of existing excipient selection strategies, and establishes a foundation for intelligent formulation design. The proposed framework aims to reduce experimental workload while supporting faster and more reproducible pharmaceutical product development.