Adsorption of biomolecules on the surface of nanomaterials: A meta-modeling approach integrating molecular modeling with machine learning

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 those 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 meta-modeling approach that integrates physics-based molecular modeling with machine learning algorithms to predict the interaction energy between NMs and biomolecules extracted from potentials 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 (QSPR). The developed QSPR model helps the understanding and predicting the interactions between NMs (including carbon-based, metals, metal oxides, metalloids, and cadmium selenide) and biomolecules thus facilitating safe and sustainable design of nanomaterials for various applications, particularly for nanomedicine. An application for surface descriptors of nanomaterials is available at GitHub.