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

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 strong generalizability on an external test set (R² = 0.91 for IFN-β; 0.85 for IL-6). This work advances sequence-based quantitative structure-activity relationship (QSAR) modeling by leveraging attention-based neural networks to eliminate the need for manual feature engineering while maintaining biological interpretability. To facilitate community access, the updated artificial immune cell (AI-cell) web-based platform is introduced, which supports rapid immune profiling of NANPs in silico. This new approach methodology provides a scalable framework to guide the rational design and optimization of NANPs through rapid prediction of their immune responses.