@inproceedings{ozturk-etal-2026-automated,
    title = "Automated Screening of Antibacterial Nanoparticle Literature: Dataset Curation and Model Evaluation",
    author = {Ozturk, Alperen  and
      {\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l  and
      Root, Sophia Bahar  and
      Violi, Angela  and
      Kotov, Nicholas  and
      VanEpps, J. Scott  and
      Emre, Emine Sumeyra Turali},
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.20/",
    pages = "454--465",
    ISBN = "979-8-89176-380-7",
    abstract = "Antimicrobial resistance is a growing global health threat, driving interest in nanoparticle-based alternatives to conventional antibiotics. Inorganic nanoparticles (NPs) with intrinsic antibacterial properties show significant promise; however, efficiently identifying relevant studies from the rapidly expanding literature remains a major challenge. This step is crucial for enabling computational approaches that aim to model and predict NP efficacy based on physicochemical and structural features. In this study, we explore the effectiveness of traditional machine learning and deep learning methods in classifying scientific abstracts in the domain of NP-based antimicrobial research. We introduce the ``Antibacterial Inorganic NAnoparticles Dataset'' AINA of 7,910 articles, curated to distinguish intrinsic antibacterial NPs from studies focusing on drug carriers or surface-bound applications. Our comparative evaluation shows that a fine-tuned BioBERT classifier achieved the highest macro F1 (0.82), while a lightweight SVM model with TF-IDF features remained competitive (0.78), highlighting their utility in low-resource settings. AINA enables reproducible, large-scale identification of intrinsically bactericidal inorganic NPs. By reducing noise from non-intrinsic contexts, this work provides a foundation for mechanism-aware screening, database construction, and predictive modeling in antimicrobial NP research."
}