Designing lipid nanoparticles using a transformer-based neural network.

ABSTRACT: The RNA medicine revolution has been spurred by lipid nanoparticles (LNPs). The effectiveness of an LNP is determined by its lipid components and their ratios; however, experimental optimization is laborious and does not explore the full design space. Computational approaches such as deep learning can be greatly beneficial, but the composite nature of LNPs limits the effectiveness of existing single molecule-based algorithms to LNPs. Addressing this, our approach integrates the multi-component and multimodal features of composite formulations such as LNPs to predict their performance in an end-to-end manner. Here we generate one of the largest LNP datasets (LANCE) by varying LNP formulations to train our deep learning model, COMET. This transformer-based neural network not only accurately

SUBMITTER: Chan A 

PROVIDER: S-EPMC12534189 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature