Molecular Surface Descriptors to Predict Antibody Developability

Abstract

Understanding the molecular surface properties of monoclonal antibodies (mAbs) is crucial for determining their function, affinity, and developability. Yet, robust methods to accurately represent the key structural and biophysical features of mAbs on their molecular surface are still limited. Here, we introduce MolDesk, a set of molecular surface descriptors specifically designed for predicting antibody developability characteristics. We assess the performance of these descriptors by directly benchmarking their correlations with an extensive array of in vitro and in vivo data, including viscosity at high concentration, aggregation, hydrophobic interaction chromatography (HIC), human pharmacokinetic (PK) clearance, Heparin retention time, and polyspecificity. Additionally, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant for electrostatic potential calculations, residue-level hydrophobicity scales, initial antibody structure models, and the impact of conformational sampling. Based on our benchmarking analysis, we propose six in silico developability rules that leverage these molecular surface descriptors and demonstrate their superior ability to predict the clinical progression of therapeutic antibodies compared to established models like TAP. 1