Predicting Antibody Stability pH Values from Amino Acid Sequences: Leveraging Protein Language Models for Formulation Optimization

Monoclonal antibodies (mAbs) offer significant therapeutic benefits; however, their formulation requires careful optimization to prevent instability. Standard practices for determining optimal formulation conditions rely on time-consuming and costly wet lab experiments. We developed a machine learning-based approach to predict the optimal pH value for stabilizing mAbs using only their amino acid sequences by leveraging a protein language model. Due to the absence of directly relevant methods, we established a baseline by comparing various combinations of elements. We also conducted feature engineering to enhance the predictive performance by incorporating structural information and descriptors. Our approach achieved a high Pearson correlation coefficient of 0.88 on the test folds from 10-fold cross-validation, highlighting its potential to complement wet lab experiments and increase the efficiency of mAb formulation.