Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning

The discovery of self-assembling molecular systems is often constrained by the high computational cost of physics-based simulations and the limited generalizability of data-driven models trained on experimental datasets. This limits our ability to efficiently navigate massive design spaces. Here, we introduce a physics-informed discovery framework that integrates free-energy calculations with sequential-pool active learning and adaptive yoked deep learning to effectively navigate vast chemical coassembly spaces. Using pairwise free energy as a physically grounded descriptor of co-assembly propensity and excipient net charge as a proxy for interfacial stabilization, our approach predicts nanoparticle-forming drug–excipient combinations without relying on experimental data for model training. Our sequential-pool yoked deep learning strategy utilizes adaptive machine learning to reduce the number of expensive simulations required while maintaining predictive accuracy, enabling screening of 18 million molecular pairs. Prospective validations identified 24 drug nanoparticles using eight previously unknown stabilizing molecules. The new nanoparticles exhibit ultra-high drug loading, effectively improve the aqueous solubility of drugs, and maintain in vitro drug efficacy. Together, this work establishes a generalizable framework coupling molecular simulations and yoked deep learning to accelerate discovery in self-assembling molecular systems.