Formulation Informatics (Nanomedicine): Deep Learning Workflows on HPC for the Predictive Design of Targeted Lipid Nanoparticles (LNPs)

The design and development of nanomedicines, with a critical emphasis on lipid nanoparticles (LNPs) for the delivery of gene therapies and mRNA vaccines, has historically relied on empirical trial-and-error processes. This iterative approach imposes a substantial economic burden and severely restricts the exploration of the vast chemical space (> 106 possible combinations). In this research, we formalize and expand the paradigm of Formulation Informatics, a convergent discipline that merges the expertise of the Pharmacobiological Chemist (QFB) in experimental pharmaceutics and lipid physical chemistry, with next-generation Artificial Intelligence (AI) architectures. We present a massive computational pipeline based on Deep Learning implemented on PyTorch and TensorFlow, designed to run on High-Performance Computing (HPC) infrastructures and GPU clusters (Data Science and Supercomputing). Fed by massive physicochemical databases, the model extracts latent representations of molecular structures to predict optimal stoichiometries that maximize Encapsulation Efficiency (EE) and direct cellular specificity. The results show a predictive accuracy of 94.2% (R 2 = 0.94), accelerating the creation of targeted therapies and reducing R&D timelines from months to hours. This technological convergence catalyzes a revolution in the profiling of health technologies.