Advances in computing and data science, such as machine learning (ML) and artificial intelligence (AI), are transforming all aspects of bioscience research and drug development. But while ML has been applied to many aspects of understanding protein structures – such as DeepMind’s AlphaFold algorithm – and in the design of proteins, enzymes and large antibodies, there’s been relatively little progress so far in applying ML tools to smaller, nimbler VHH antibodies.

To fill this gap, we’ve teamed up with academic researchers at the University of Nottingham and Aston University to apply ML tools to our own way of working through a new Knowledge Transfer Partnership (KTP), funded by UKRI through Innovate UK. KTPs aim to help businesses improve their competitiveness and productivity through the better use of knowledge, technology and skills within the UK knowledge economy.

The implementation of these technologies is being led by molecular biologist Dr Ben Wagstaffe, who joins us as the KTP Associate from the University of Nottingham. He previously gained his PhD working in Dr Anna Hine’s group at Aston University who helped develop the Colibra™ technology used in our early antibody libraries.

Ben will be connecting his academic colleagues together with our antibody discovery and engineering experts to apply ML tools across our VHH antibody discovery and engineering pipeline to speed up and improve the ways in which we discover and optimise antibodies for biotherapeutic use.

It’s still early days for this project, but we’re excited to see how it unfolds over the coming years.

“By the time I retire, machine learning and AI will have transformed the whole of biology. We’ll be much more efficient, using our computing tools to generate new ideas and hypotheses that can be tested quickly in the lab, rather than relying on ‘wet’ research for discovery, getting us to effective solutions and new treatments faster than ever before.” Dr Ben Wagstaffe