Advances in database resources and computational methods for predicting antibody polyreactivity
TL;DR
This review compiles the data resources related to polyreactive antibodies and places a particular emphasis on computational models for predicting antibody polyreactivity, which includes empirical models based on physicochemical properties, traditional machine learning models, deep learning networks, and protein language models.
Abstract
Antibody polyreactivity refers to the ability of a monoclonal antibody to non-specifically bind to a diverse range of antigens. While this property may be an intrinsic mechanism of the immune response, it poses significant challenges in therapeutic antibody development, often leading to off-target effects, poor pharmacokinetics, and potential toxicity. This review compiles the data resources related to polyreactive antibodies and places a particular emphasis on computational models for predicting antibody polyreactivity. The latter includes empirical models based on physicochemical properties, traditional machine learning models, deep learning networks, and protein language models. Through delineating the complexity of antibody polyreactivity, this review emphasizes the critical role and growing potential of computational prediction tools in selecting and engineering antibody drug candidates at early stages, thereby reducing development risks and accelerating the development of safer and better therapeutic antibodies.