Molecular modelling–driven formulation design for targeted pulmonary drug delivery

• AI-driven formulation design accelerates nanocarrier optimization, aerosol performance, and personalized dosing strategies for targeted pulmonary delivery. • Integration of multi-omics, imaging informatics, and systems biology advances targeted lung therapeutics. • In-silico tools predict drug–polymer interactions, stability, and controlled release before experimentation • Molecular modelling guided rational design of precise pulmonary drug delivery. • Computational–experimental convergence streamlines translation from in-silico design to clinical validation. Molecular modelling an impactful and trending approach for rational design of pulmonary drug delivery systems. In particular, in silico techniques can screen candidates and optimise formulations early, reducing trial-and-error and improving the likelihood of clinical translation. Therefore, this review highlights the application of in silico methods in critical formulation decisions for achieving targeted delivery, controlled release, and enhanced therapeutic efficacy in the lungs. The application of molecular docking, quantum mechanics/molecular mechanics, and molecular dynamics simulations to predict drug–excipient and drug–polymer interactions to optimize particle engineering and improve stability of resulting formulations. Modelling of active pharmaceutical ingredients with carriers, surfactants, and nanocarriers such as liposomes, polymeric nanoparticles, and micelles was designed for deep lung delivery. The integration of molecular modelling approaches for predicting the physicochemical properties based on pharmacokinetic modelling for analysing their behaviour, regional lung deposition, and systemic exposure at the molecular level was explored. Challenges such as model validation, treatment of complex biological interfaces, and data–driven personalization are critically analysed. Overall, the article underscores molecular modelling as a crucial component of next-generation, data-based formulation development pipelines for targeted pulmonary drug delivery and outlines future opportunities for AI- and machine-learning–augmented design.