Accelerating antibody development: sequence and structure-based models for predicting developability properties via size exclusion chromatography.

ABSTRACT: Experimental screening for biopharmaceutical developability properties typically relies on resource-intensive, and time-consuming assays such as size exclusion chromatography (SEC). This study highlights the potential of in silico models to accelerate the screening process by exploring sequence and structure-based machine learning techniques. Specifically, we compared surrogate models based on pre-computed features extracted from sequence and predicted structure with sequence-based approaches using protein language models (PLMs) like ESM-2. In addition to different end-to-end fine-tuning strategies for PLM, we have also investigated the integration of the structural information of the antibodies into the prediction pipeline through graph neural networks (GNN). We applied these different me

SUBMITTER: Abeer ANMN 

PROVIDER: S-EPMC12477876 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature