Predicting biomolecule adsorption on nanomaterials: a hybrid framework of molecular simulations and machine learning

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: R² = 0.84, RMSE = 1.52, R_cv² = 0.83, and RMSE_cv = 1.34; validation set: R² = 0.70, RMSE = 1.94, and R_cv² = 0.72, RMSE_cv = 1.88) helps in understanding and predicting the interactions between NMs (including carbon-based materials, metals, metal oxides, metalloids, and cadmium selenide) and biomolecules (including amino acids and amino acid derivatives). The model facilitates safe and sustainable design of nanomaterials for various applications, particularly for nanomedicine, by providing insight into nano–bio interactions, identification of toxicity risk and optimizing functionalization for safety according to the risk mitigation policy of regulatory authorities. Additionally, a dedicated application has been developed and is available on GitHub, enabling researchers to analyze the surface properties of nanomaterials belonging to the above-mentioned groups of NMs.