AI-Driven Molecular Design for Targeted Drug Delivery

Abstract

Targeted drug delivery is critical for improving therapeutic efficacy while minimizing off target effects. Conventional nanocarrier design relies on empirical approaches often leading to sub-optimal performance and prolonged development. Here we present an AI driven molecular design framework that integrates large scale molecular data Graph Neural Networks  transformer based models  and reinforcement learning for multi objective optimization. Screening over 100,000 candidate polymers and lipids the framework identified nanocarriers with optimal particle size  surface charge and drug loading efficiency. AI designed systems demonstrated stimuli responsive release under pH ,redox and enzymatic triggers, enhancing targeting specificity and predicted pharmacokinetics. Iterative active learning improved predictive accuracy and reduced uncertainty while explain-ability analysis highlighted key molecular determinants such as hydrophobic hydrophilic balance and linker flexibility. Bench-marking across oncology , mRNA delivery and neurological models showed strong concordance with experimental data. This study demonstrates that AI driven nanoarchitectonics enables adaptive, efficient and clinically relevant drug delivery systems offering a scalable approach for precision nanomedicine.