Fast and accurate generative AI design of novel antibodies extends Exscientia’s capabilities beyond small molecules
Sequencing paired human antibody data to create better AI models for antibody design
Automated laboratory with proprietary hardware to enable integration of AI design with high-throughput biologics profiling under development
Exscientia plc announced the expansion of its platform to include the design of biologics, such as human antibodies. The Company has progressed AI-driven capabilities for virtual biologics design throughout the year and is now establishing an automated biologics laboratory in Oxford to internally generate and profile novel antibodies.
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“Exscientia is integrating biologics design into the modular architecture of our end-to-end, patient-first AI platform. Having demonstrated the ability of our existing precision medicine patient tissue models to analyse novel antibodies, we believe the addition of this biologics design capability will create one of the most powerful antibody platforms available”
“Exscientia is integrating biologics design into the modular architecture of our end-to-end, patient-first AI platform. Having demonstrated the ability of our existing precision medicine patient tissue models to analyse novel antibodies, we believe the addition of this biologics design capability will create one of the most powerful antibody platforms available,” said Professor Andrew Hopkins, Exscientia’s founder and Chief Executive Officer. “Our balanced business model allows us to advance our pipeline programmes while also investing in platform growth. Expanding into biologics enables us to nearly double the addressable target universe of our platform. Over the past two years, antibodies represented eighteen of the top fifty drugs measured by revenue, and by adding the capability to design new human antibodies with AI and automation, we believe we will be able to develop the most effective drug for patients, regardless of modality.”
Current approaches to optimise antibodies, even those that use machine learning, still depend on discovering antibodies by experimental screening methods. Combining generative AI design and virtual screening of biologics would allow investigation of a broader antibody space and support Exscientia’s goal to design all of its biologics de novo for specific target epitopes without the need for screening.
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In order to design novel antibodies against specific protein epitopes, it is necessary to generate accurate models of their structure at speed and scale. Initial versions of the technology invented by Professor Charlotte Deane, Exscientia’s Chief Scientist of Biologics AI, produced accurate protein modelling up to 35,000 times faster than Alphafold2 (Abanades et al. Bioinformatics 2021). Exscientia has significantly expanded the scope, speed and integration of these algorithms while also integrating the capabilities into its broader platform. Exscientia’s virtual screening methodology for antibodies is now over three times more accurate than the published state of the art.
Key to Exscientia’s AI approach is using the knowledge of the observed human antibody space to optimise biologics for clinical development. The binding site of the antibody consists of two chains (heavy and light). Typically, sequencing of antibodies has been limited to single chains, losing the true biology of antibodies. The Company is building a proprietary database of paired chain sequences to better understand the complex biology of antibodies in a more natural environment. Exscientia is using this data for machine learning in order to describe and model human antibody space more accurately.
Exscientia’s new laboratory facilities, which add an additional 8,000 square feet to its headquarters in the Oxford Science Park, will automate the production of proprietary data for each antibody, measuring essential qualities including affinity, immunogenicity, aggregation and stability. Exscientia’s engineers are building proprietary automation hardware to enable high-throughput antibody profiling to support its predictive model building for multi-parameter optimisation.
“Our strategy is to replace current experimental discovery techniques with precision engineered de novo design of optimised, fully-human biologics,” said Professor Charlotte Deane MBE. “Current methods limit the discovery of new binding sites to what can be explored via animal immunisation or laboratory-based libraries. By virtually designing all aspects of a biologic in silico, we can explore a much broader target universe and create more precisely targeted therapeutics: Biologics by design not discovery, driven by AI and automated experiment, is our approach.”
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