Characterizing lipid nanoparticle self-assembly and structure using coarse-grained simulations

mRNA/lipid nanoparticle (LNP) technology underlies successful COVID-19 vaccines and emerging therapeutics, but researchers needed a detailed structural understanding of mRNA-containing LNPs to optimize them. Schrodinger scientists used the Materials Science Suite and Desmond molecular dynamics engine to automate coarse-grained model parameterization from a 1.5-microsecond atomistic reference simulation with the OPLS4 force field, built homogeneous systems at specific lipid ratios (45.6/9.4/43.4/1.6 mol% of ALC-0315/DSPC/cholesterol/ALC-0159), and ran Langevin dynamics tracking LNP morphology as pH shifted from acidic formation conditions to neutral blood pH. The work validated a coarse-grained model that captures LNP self-assembly from homogeneous starting conditions, reproduced pH-dependent component distribution matching experiments, and observed spontaneously formed bleb structures in RNA-containing LNPs for the first time; findings were published in Molecular Pharmaceutics (2024, 21, 4747-4753).

The customer is anonymised in Schrödinger’s own case study — not withheld by us.

Schrödinger is a physics-plus-machine-learning computational platform for molecular design; all top-20 pharma companies by revenue are customers of its drug-discovery software.

This sits in the Build stage of the AI use-case lifecycle: Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.