📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies (2019)

First Author: Hebditch M

Attributed to: Enabling rapid liquid and freeze-dried formulation design for the manufacture and delivery of novel biopharmaceuticals funded by EPSRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1101/625830

Publication URI: http://dx.doi.org/10.1101/625830

Type: Preprint