SurfProp: A surface-based property prediction framework for antibody developability and screening

Pranav Rao, Henry Isaacson, Jennifer L. Hofmann, Darcy Davidson, Amy Wang, Andrew Martin Watkins, Richard Bonneau, Saeed Izadi, Jae Hyeon Lee

Keywords: antibody, property prediction, developability, drug discovery

TL;DR: SurfProp is a surface-based property prediction framework for fast biophysical property calculations with applications in screening and guidance of generative designs

Abstract: Therapeutic antibodies are an important class of drugs, increasingly used to treat a variety of conditions. Developing antibodies is challenging in part due to issues with viscosity, aggregation, solubility and more \emph{developability} properties that pose issues for manufacturing and delivery. As these properties require large-scale experiments to measure, \textit{in silico} biophysical molecular descriptors are often considered by proxy, and serve as a basis for screening and optimization. In this work, we introduce SurfProp, a surface-based, differentiable property prediction framework aimed at improving the antibody developability workflow in two ways. (1) The \textit{insilico} arm of SurfProp predicts electrostatics and computes hydrophobicity with a significant speedup over traditional methods, facilitating higher throughput \textit{in silico} property screening. (2) the \textit{experimental} arm of SurfProp uses the pre-trained model from the \textit{in silico} task to predict experimental developability properties; here we demonstrate the ability of the pre-trained model to learn hydrophobic interaction chromatography (HIC) more effectively than a model trained from scratch.

Submission Number: 132