{
    "abstract": "The adsorption of biomolecules on the surface of nanomaterials (NMs) is a critical determinant of their behavior, toxicity, and efficacy in biological systems. Experimental testing of these phenomena is often costly or complicated. Computational approaches, particularly the integrating methods of various theoretical levels, can provide essential insights into nano-bio interactions and bio-corona formation, facilitating the efficient design of nanomaterials for biomedical applications. This study presents a hybrid, meta-modeling approach that integrates physics-based molecular modeling with machine learning algorithms to predict the interaction energy between NMs and biomolecules extracted from the potential of mean force (PMF). Novel descriptors for the surface properties of NMs are developed and combined with structural descriptors of biomolecules to derive quantitative structure-property relationships (QSPRs). The developed QSPR model (training set: ",
    "authors": [
        {
            "affiliation": "QSAR Lab Ltd, Trzy Lipy 3, 80-172 Gdansk, Poland. e.wyrzykowska@qsarlab.com.",
            "firstname": "Ewelina",
            "initials": "E",
            "lastname": "Wyrzykowska"
        },
        {
            "affiliation": "QSAR Lab Ltd, Trzy Lipy 3, 80-172 Gdansk, Poland. e.wyrzykowska@qsarlab.com.",
            "firstname": "Mateusz",
            "initials": "M",
            "lastname": "Balicki"
        },
        {
            "affiliation": "QSAR Lab Ltd, Trzy Lipy 3, 80-172 Gdansk, Poland. e.wyrzykowska@qsarlab.com.\nFaculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.",
            "firstname": "Iwona",
            "initials": "I",
            "lastname": "Anusiewicz"
        },
        {
            "affiliation": "School of Physics, University College Dublin, Belfield, Dublin, Ireland.",
            "firstname": "Ian",
            "initials": "I",
            "lastname": "Rouse"
        },
        {
            "affiliation": "School of Physics, University College Dublin, Belfield, Dublin, Ireland.",
            "firstname": "Vladimir",
            "initials": "V",
            "lastname": "Lobaskin"
        },
        {
            "affiliation": "QSAR Lab Ltd, Trzy Lipy 3, 80-172 Gdansk, Poland. e.wyrzykowska@qsarlab.com.\nFaculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.",
            "firstname": "Piotr",
            "initials": "P",
            "lastname": "Skurski"
        },
        {
            "affiliation": "QSAR Lab Ltd, Trzy Lipy 3, 80-172 Gdansk, Poland. e.wyrzykowska@qsarlab.com.\nFaculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.",
            "firstname": "Tomasz",
            "initials": "T",
            "lastname": "Puzyn"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.1039/d4nr05366d",
    "journal": "Nanoscale",
    "keywords": [],
    "methods": null,
    "publication_date": "2025-04-11",
    "pubmed_id": "40211956",
    "results": null,
    "title": "Predicting biomolecule adsorption on nanomaterials: a hybrid framework of molecular simulations and machine learning.",
    "xml": "<Element 'PubmedArticle' at 0x7885031da3e0>"
}