BB-EIT: A Generalized Prediction Model for Protein Adsorption on Polymer Brushes Using Augmented Chemical Embeddings

Precise control of protein adsorption on polymer surfaces
is essential
in materials science and biomaterial design, with applications in
antifouling materials, biosensors, cell culture, and drug delivery
systems. However, the complex interactions between polymers and proteins
and the limited availability of high-quality interaction data remain
major challenges in polymer informatics. Current approaches often
lack the generalizability needed to model diverse polymer–protein
systems within a single unified framework, and there is a paucity
of comprehensive predictive models capable of handling diverse polymer–protein
interactions. To address these challenges, we introduce BB-EIT (Biointerface
BERT Encoder for Interaction Translation), a novel generalized model
designed to accurately predict the amount of diverse protein adsorption
on polymer brushes. BB-EIT leverages the pretrained ChemBERTa large
language model (LLM) architecture using SMILES strings for robust
chemical representation and convenient data augmentation through SMILES
enumeration. By adapting the pretrained model with an extended layer
integrating a comprehensive set of physicochemical and biochemical
features, including polymer thickness, water contact angle, and surface
charge as well as protein isoelectric point (pI) and size, the BB-EIT
showed state-of-the-art performance and strong generalizability. The
model accurately predicted the adsorption behavior in previously unseen
polymer and protein systems. This work represents an important step
toward the data-driven design of biomaterials with tailored protein
adsorption properties.