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ANTIBODY
drug discovery
in 1 day

ai meets drug discovery

 

Antiverse: Leveraging AI to Achieve Antibody Drug Discovery in One Day

BETTER MEDICINES FASTER

Antiverse is building a world-first computational antibody drug discovery platform to predict antibody-antigen binding and provide antibody drug candidates in one day.

A combination of state of the art machine learning and cell-free protein synthesis is used to predict antibodies that bind to a given antigen target with high affinity. The resulting software can then take antigen target sequence, provided by the customer, and do a high-throughput screening of all possibilities of antibody sequences to detect the sequence that will produce a high-affinity antibody for the target. The customer will be provided with the sequence in a single day, thus reducing the time typically required for antibody therapeutics discovery by 3 to 18 months.

 

EMAIL

info@antiverse.io

 

Location

Caerphilly
United Kingdom

 

 

Innovation: Combining deep learning with cell-free protein synthesis

AI Revolution in biotech

We are at the cutting edge, leveraging recent advances in deep learning that allow prediction of antibody-antigen binding based solely on the primary structure of the proteins, and harnessing developments in cell-free protein synthesis technology that enable the high-throughput generation of a large, customised proprietary data set to train our bespoke algorithm.

What has hindered the ability to model antibody-antigen interactions so far is both the lack of a suitable data set and the inherent limitations of traditional modelling methods.

The first recent advance is in machine learning. Existing modelling methods typically rely on human expertise to decide which features are relevant, whereas deep learning techniques are successful in great part because they learn directly from the entire dataset. Efforts to predict binding were previously based on 3D structures but the largest publicly available data set, abYsis, only contains around 200 of these, which is far too small for modelling. We are developing and applying recent advances in machine learning (primarily deep learning) to be able to use the amino acid sequence of the antibody and antigen as the data input.

The second recent shift is the recent improvement in cell-free protein synthesis. This enables us to generate a large in-house data set of antibody-antigen binding values, coupled to sequence data, with a high-throughput method. A large data set is required for machine learning, but companies offering traditional methods of antibody discovery have very limited data sets because they do not require information about the primary amino acid sequences or 3D structures involved. Thus, the combination of these two breakthroughs opens up the potential to effectively predict antibody sequences for a target of interest. We have close contact with customers who are keen to trial our service, and we have completed a proof of concept on small dataset to understand the technical challenges.

 


LAB Location 

Cambridge
United Kingdom

 

 

ABOUT US

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The team consists of Ben Holland, Dr Rowina Westermeier and Murat Tunaboylu, and the project has gradually drawn in collaborators, advisors, potential clients and investment.

Ben Holland has an MEng in Engineering Science from the University of Oxford and experience in mathematical modelling, especially neural networks, and will use the generated data to train the machine learning system.

Rowina has a PhD in Biochemistry from the University of Cambridge in cell-free protein synthesis and will be using this cutting-edge technique for the high-throughput generation of antibody fragments. She previously worked as a protein synthesis scientist for drug development for the contract research organisation Domainex.

Murat Tunaboylu has a BSc in Electrical Engineering and twelve years of experience as a software engineer. He also has extensive experience in labware automation and will be automating the lab work and creating the software that customers will use.

Our academic collaborator and advisor, Dr. Andrew Martin, Reader at University College London brings nearly 30 years of antibody research to the table. He is the creator of abYsis, the largest publicly available database for antibody sequences and 3D structures and has led the development and commercialisation of an automated programme for modelling antibodies.

 

JOIN OUR TEAM

Antiverse is accelerating antibody drug discovery with AI. Join our talented ambitious team to be a part of this revolution!

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Phage display scientist

Join us to work on a green field project. Set-up a phage display pipeline in our lab. Buy instruments, design experiments,  feed our AI, and change the world!

Send us your CV to have a chat: careers@antiverse.io

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MACHINE LEARNING ENGINEER

Come and join us in our journey to change drug discovery! We are seeking for a machine learning engineer with a focus on structural biology.

Send us your CV to have a chat: careers@antiverse.io

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GENERAL APPLICATION

Is the role you are looking for not listed?
Drop us an email to have a speculative chat.

Send us your CV to have a chat: careers@antiverse.io