From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.

ABSTRACT: Nucleic acid nanoparticles (NANPs) represent a versatile platform for drug delivery and modulation of therapeutic responses. To expedite NANPs' translation from bench to bedside, rapid coordination of their design principles with immunostimulatory assessment is essential. Here, a deep learning framework is presented to predict cytokine responses, specifically interferon-beta (IFN-β) and interleukin-6 (IL-6), induced by NANPs in human microglial cells based solely on their sequences. Using a transformer-based architecture augmented through systematic strand permutation trained on 176 structurally diverse, individually assembled, and experimentally characterized NANPs, the model achieved high predictive performance in cross-validation (R² = 0.96-0.97, RMSE ≤ 0.01) and demonstrated

SUBMITTER: Johnson MB 

PROVIDER: S-EPMC12710182 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature