Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development

Nanoparticles (NPs), due to their small size and large surface area, have advanced their use as drug carriers for delivering various therapeutic molecules. When entering biological environments, nanoparticles typically adsorb proteins, forming a surface layer known as a protein corona that significantly affects the biological and therapeutic functions of a delivery system. Understanding and predicting protein adsorption is essential for optimizing nanoparticle design in drug delivery, diagnostics, and therapy. Machine learning and deep learning (ML/DL) offer promising methods for designing nanoparticles with specific properties, particularly given recent advancements in computation and nanoparticle analysis. This review explores ML/DL studies of nanoparticle-protein interactions and emphasizes the popularity of Random Forest (RF) and Deep Learning (DL) models in predicting protein corona compositions. RF models are highly valued for managing high-dimensional data and offering interpretability, which helps identify key NP features influencing protein adsorption. Conversely, DL excels at modeling non-linear relationships and detecting subtle interaction patterns. While most current research focuses on protein coronas, future models may also include other biocorona components. This is particularly relevant for soft materials, such as lipid nanoparticles (LNPs), which are now approved for delivering mRNA and peptide-based vaccines. Our findings underscore the need for advanced modeling techniques and high-quality, diverse experimental data to drive innovations in nanomedicine. Combining RF and DL approaches leverages their complementary strengths to overcome the challenge of limited experimental data and further improve NP designs for biomedical use.