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Helical aromatic oligoamide foldamers (1a–c) with tunable lengths were computationally examined for their ability to bind selected sugars and sugar alcohols. These helices feature cylindrically shaped inner cavities lined with multiple inward-facing amide carbonyl oxygens acting as hydrogen-bond acceptors, enabling sugar binding via hydrogen bonding. Each of the helical foldamers has an overall dipole moment that increases with the length of the helix. The binding of a guest typically results in a reduction of the overall helix dipole moment within the complex, although there are several exceptions. The strength of host–guest interactions correlated positively with the number of hydrogen bonds formed. Longer helix 1c showed stronger interaction energies (up to −84.45 kcal mol−1), particularly with disaccharides, while shorter helix 1a bound sugars more weakly due to fewer established hydrogen bonds. The helical hosts exhibit structural adaptibility upon binding guests, with host distortion upon binding decreased with increasing helix length. Despite reduced binding energies, the complexes retained binding capability in aqueous environments, demonstrating their viability for aqueous-phase applications. This study underscores the critical roles of helical length and dipole alignment in optimizing sugar binding, providing a theoretical foundation for designing synthetic receptors for sugars and sugar alcohols based on aromatic oligoamide foldamers. 

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 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 NANPsin 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.