A Guided Design Framework for the Optimization of Therapeutic-like Antibodies

Abstract

Antibodies must meet stringent developability criteria for successful commercialization—a challenge for machine learning approaches given the limited available data. Selecting candidates with biophysical properties similar to clinical-stage antibodies offers an alternative to data-intensive approaches. However, such methods typically suffer from limited throughput due to structure-based calculations and can eliminate promising candidates through overly strict filtering. By benchmarking classical filtering methods against experimental datasets, with viscosity as a proof-of-concept, we identify an informative set of biophysical definitions (relevant to charge and hydrophobicity). Using these as optimization objectives for guided design, we introduce TherAbDesign, a sequence-based framework that evaluates and optimizes antibodies for developability without requiring structure prediction or physics-based computation. TherAbDesign proposes rational modifications to mimic the properties of successful therapeutic antibodies, which we demonstrate can improve known developability liabilities like high viscosity without explicitly modeling their mechanism of action.