The application of messenger RNA (mRNA) as a drug modality is enabling new treatment options for infectious diseases, cancers, as well as immunological and neurological disorders. Much of this emerging field is driven by the ability of mRNA drugs to directly yield programmable therapeutic proteins in vivo. Nevertheless, mRNA is susceptible to chemical and enzymatic degradation, and its large size and charge prohibit passive cellular permeation on its own. Thus, efficient delivery tools are required to ensure high transfection efficiency and low toxicity of mRNA therapeutics. Current mRNA delivery technologies include both viral and non-viral delivery vehicles that protect therapeutic materials from degradation. Viral vectors for mRNA delivery have seen success as delivery vehicles. However, they are also limited by immunogenic responses that can obstruct delivery and complicate re-dosing. At the same time, non-viral synthetic alternatives, comprised of polymers and lipid materials, have recently been developed to encapsulate mRNA. The current state-of-the-art lipid-based non-viral vectors are ionizable lipid nanoparticles (LNPs), which have famously been incorporated by Pfizer and Moderna to express SARV-CoV-2 spike protein and are currently used in over 70 clinical trials for mRNA therapeutics delivery. While the general concept of LNP formulations is already well developed, significant challenges remain on how to optimize the loading/transfection efficiency and biodistribution of LNPs with improved manufacturing/formulation processes. Specifically, it has been found that not only the detailed chemical nature of the ionizable lipids but also the type and concentrations of the helper lipids, as well as their relative cholesterol content, can result in different LNP sizes and delivery properties inherent to each formulation. This includes composition-dependent tissue delivery that has been used to control the biodistribution of mRNA payloads to different tissues in a selective manner. Additionally, the solution pH and buffers used during manufacturing can play an equally important role in dictating LNP properties. For example, it has been observed that LNP/mRNA systems produced in sodium citrate buffer (300 mM, pH ≈ 4) demonstrated the induction of bleb structures which increased mRNA loading, enhanced the integrity of encapsulated mRNA, and improved transfection, while manufacturing in buffered sodium acetate and sodium citratephosphate solutions (both 300 mM, pH ≈ 4) led to less effective LNP formulations. Figure . Machine-learning (ML) vs. structure-based modeling of LNPs. While ML-models (A) are able to rapidly predict the averaged effective pKa of ionizable lipids in LNP environments, they provide limited physical insight into fundamental structure-activity relationships of LNPs. At the same time, structure-based, all-atom (AA) fixed-protonation state models (B) make it difficult to correctly model the variable, dynamic protonation states of ionizable lipids in LNPs in different local conformations and environments, although some general information about their preferred conformations/membrane positions in their protonated/deprotonated forms can still be obtained from such models. Variable protonation state AA CpHMD models 28 (C) can overcome these challenges by allowing the system to dynamically sample the protonated/deprotonated configurations of each ionizable lipid in an environment-/conformationdependent fashion, described by an additional -coordinate for each ionizable site in the system. While such CpHMD models have been applied successfully to study the interactions of peptides and proteins with membranes, as well as to lipid bilayer LNP models, this work (D) now applied CpHMD models to predict the structures and charge distributions of the interior portions of LNPs. The systems modeled reflect the interior of the LNP vehicle, approximated with periodic boundary conditions, as shown in the figure. With the CpHMD models, environment-dependent protonation of the ionizable lipids, as well as pH-dependent structures, are observed, providing a rational, structure-based approach to predict the charge distributions of LNPs, which we hypothesize influences the RNA loading, biocorona formation, and the transfection efficiency of LNPs. Components for AA simulations and representative cartoons are colored as shown. Images for insets (A), 26 (B), and (C) are used with permission from their respective publishers. Additionally, the image for inset (C) was recolored for clarity to match the scheme of insets (B) and (D).