Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation.

ABSTRACT: Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region

SUBMITTER: Rai BK 

PROVIDER: S-EPMC9941094 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature