Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions

Area of Science:

• Nanomedicine
• Biomaterials Science
• Computational Biology

Background:

• The protein corona (PC) significantly influences nanoparticle (NP) behavior, affecting pharmacokinetics, safety, and efficacy in drug and vaccine delivery.
• Challenges in understanding NP-PC interactions stem from numerous physicochemical and experimental variables, hindering pattern identification.
• Machine learning (ML) offers powerful tools for analyzing complex NP-PC datasets and uncovering predictive patterns.

Purpose of the Study:

• To systematically review machine learning applications in nanoparticle-protein corona research.
• To identify key ML workflows for predicting NP-PC interactions, including composition, dynamics, and biological outcomes.
• To outline future directions for enhancing ML models in nanomedicine.

Main Methods:

• Conducted a systematic literature review of ML-based NP-PC analyses from PubMed and Web of Science (2000-2025).
• Analyzed identified ML workflows focusing on prediction of PC formation, composition, and dynamics.
• Examined ML's role in predicting pharmacologically and toxicologically relevant endpoints like biodistribution and cellular uptake.

Main Results:

• Identified increasing use of ML algorithms to analyze NP-PC interactions and predict outcomes.
• ML workflows show promise in predicting PC composition, formation dynamics, NP biodistribution, and cellular uptake.
• Key developments in ML approaches for understanding nano-bio interactions were discussed.

Conclusions:

• ML holds significant potential for advancing the analysis of nanoparticle-protein corona interactions.
• Future research should focus on improving dataset diversity, standardizing analytical protocols, and enhancing model transparency.
• Optimizing ML approaches will be crucial for robust NP design and accurate prediction of nano-bio interactions.