Designing lipid nanoparticles using a transformer-based neural network
Alvin Chan, Ameya R. Kirtane, Qing Rui Qu, Xisha Huang, Jonathan Woo, Deepak A. Subramanian, Rajib Dey, Rika Semalty, Joshua D. Bernstock, Taksim Ahmed, Rowan Honeywell, Charles Hanhurst, Isaac Diaz Becdach, Leah S Prizant, Art Brown, Hao Song, J. L. Cobb, Louis DeRidder, Bruna dos Santos, Miguel Jiménez, Michelle T. Sun, Yuebin Huang, Ceara Byrne, Giovanni Traverso
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 predicts the efficacy of LNPs but is adaptable to non-canonical LNP formulations such as those with two ionizable lipids and polymeric materials. Furthermore, COMET can predict LNP performance in a cell line outside of LANCE and predict LNP stability during lyophilization using only small training datasets. Experimental validation showed that our approach can identify LNPs that exhibit strong protein expression in vitro and in vivo, promising accelerated development of nucleic acid therapies with extensive potential across therapeutic and manufacturing applications.