The recent paper co-authored by M. Alperen Öztürk, Şaziye Betül Özateş, Sophia Bahar Root, Angela Violi, Nicholas A. Kotov, J. Scott VanEpps, and Emine Sümeyra Turalı Emre addresses a major challenge in antimicrobial nanomedicine: automatically identifying research on inorganic nanoparticles with intrinsic antibacterial activity from a rapidly growing literature. The study introduces the AINA dataset, a carefully curated collection of 7,910 articles, and evaluates a range of machine learning and deep learning methods for screening this literature. Among the tested models, BioBERT achieved the best performance with a macro F1 score of 0.82, while a lightweight SVM model with TF-IDF features also delivered competitive results, highlighting the promise of AI-assisted literature screening for accelerating data-driven discovery in antibacterial nanoparticle research. The work appears in the Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026). For details, please refer to:

https://aclanthology.org/2026.eacl-long.20/