Author:
Chen Xingyao,Dougherty Thomas,Hong Chan,Schibler Rachel,Zhao Yi Cong,Sadeghi Reza,Matasci Naim,Wu Yi-Chieh,Kerman Ian
Abstract
AbstractAntibodies are prominent therapeutic agents but costly to develop. Existing approaches to predict developability depend on structure, which requires expensive laboratory or computational work to obtain. To address this issue, we present a machine learning pipeline to predict developability from sequence alone using physicochemical and learned embedding features. Our approach achieves high sensitivity and specificity on a dataset of 2400 antibodies. These results suggest that sequence is predictive of developability, enabling more efficient development of antibodies.
Publisher
Cold Spring Harbor Laboratory
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