Structure-Based Prediction of Molecular Interactions for Stabilizing Volatile Drugs.

ABSTRACT: Background/Objectives: The high volatility of volatile drugs significantly restricts their clinical applicability. Although excipients capable of strong interactions can reduce volatilization, conventional screening methods rely on empirical trial-and-error, resulting in low efficiency and high resource consumption. To address this limitation, this study introduces an artificial intelligence (AI)-driven strategy for screening drug-excipient interactions. Using d-borneol as a model drug, this approach aims to efficiently identify strongly interacting excipients and develop stable nano-formulations. Methods: High-throughput simulations were performed using the Protenix structure prediction model to evaluate interactions between d-borneol and 472 FDA-approved excipients. The top

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12845388 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature