Predicting Subcutaneous Antibody Bioavailability Using Ensemble Protein Language Models

Monoclonal antibodies are pivotal in modern therapeutics, yet predicting their subcutaneous bioavailability remains challenging due to the intricacies of the SC environment and the limitations of traditional experimental models. In this study, we introduce a novel machine learning framework that leverages protein language models (PLMs) to derive high-dimensional embeddings directly from antibody sequences. Using three distinct PLMsantiBERTy, ABlang, and ESM-2we extracted numerical representations that were subsequently refined via feature selection and dimensionality reduction. A systematic evaluation of multiple classifiers using Leave-One-Out cross-validation led us to develop a robust ensemble model based on a tuned support vector machine classifier, which achieved a validation accuracy of 89%. This ensemble approach, which aggregates predictions across antibodies, outperforms prior computational methods. To facilitate broad accessibility, we deployed the model as a web application, SubQAvail, enabling rapid bioavailability predictions from input antibody sequences. Our findings demonstrate the potential of integrating PLM-derived features with ensemble learning to enhance the predictive accuracy and scalability of mAb bioavailability assessment, thereby accelerating the therapeutic development pipeline.