@article{c156b0fb-9652-4588-9ecf-c72b48e8ccf5,
  abstract     = {{<p>Background/Objectives: The stimulator of interferon genes (STING) pathway plays a central role in innate immune signaling and represents an attractive therapeutic target for cancer immunotherapy. Amidobenzimidazole (ABZI) derivatives have emerged as promising non-nucleotide STING agonists with improved drug-like properties compared to cyclic dinucleotides. However, current ABZI compounds still exhibit limited oral bioavailability and cross-species potency discrepancies. In addition, potential systemic toxicity remains a concern, indicating the need for further structural optimization. Methods: In this study, a comprehensive computer-aided drug design strategy was employed to systematically investigate ABZI derivatives and identify novel STING agonists with enhanced activity and favorable pharmacokinetic profiles. A 3D quantitative structure–activity relationship (3D-QSAR) model was constructed using the Topomer CoMFA approach based on a dataset of 109 reported ABZI compounds. Guided by the contour map analysis, new chemical groups were introduced through a fragment growth method, generating a large virtual library. The library was subsequently filtered via molecular docking, molecular dynamics simulations, and MM-PBSA binding free energy calculations. Results: Among the newly designed ABZI compounds, five compounds displayed lower binding free energies than D59, with M13 and M44 showing reductions exceeding 6.7 kcal/mol. This work demonstrates the effectiveness of an integrated in silico design strategy for the discovery of novel STING agonists. Conclusions: The identified compounds represent promising candidates for subsequent experimental validation and may support the development of nitrogen heterocycle-based STING agonists for antitumor applications.</p>}},
  author       = {{Ren, Houcheng and Jin, Yuhong and Zhao, Baipu and Peng, Xiangbing and Zhao, Shan and Wang, Meiting}},
  issn         = {{1424-8247}},
  keywords     = {{3D-QSAR; ABZI; AI synthesis prediction; free energy; molecular dynamics; STING agonists}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  series       = {{Pharmaceuticals}},
  title        = {{In Silico Discovery of ABZI Nitrogen Heterocycle STING Agonists via 3D-QSAR, Molecular Dynamics, and AI-Based Synthesis Prediction}},
  url          = {{http://dx.doi.org/10.3390/ph19030387}},
  doi          = {{10.3390/ph19030387}},
  volume       = {{19}},
  year         = {{2026}},
}