Alchemab Announces Publication of AntiBERTa, an Antibody-Specific Machine Learning Model with Multiple Applications

BOSTON & CAMBRIDGE, England–(BUSINESS WIRE)–Alchemab Therapeutics, a biotechnology company focused on the discovery and development of naturally-occurring protective antibodies and immune repertoire-based patient stratification tools, today announced the publication of research demonstrating the potential of AntiBERTa (Antibody-specific Bi-directional Encoder Representation and Transformers), a transformer neural network that reads the components of an antibody amino acid sequence, to deeply understand the structure and function of antibody sequences. The article, titled “Deciphering the language of antibodies using self-supervised learning” has been published online in the journal Patterns. AntiBERTa is a 12-layer transformer model that provides a contextualized numeric representation of antibody sequences and learns biologically relevant information.

“AntiBERTa forms the basis of Alchemab’s machine learning platform, providing a pre-trained model which is primed for multiple downstream tasks relevant to antibody drug discovery,” said Jake Galson, Ph.D., Head of Technology at Alchemab. “We have already demonstrated the utility of AntiBERTa for binding-site prediction, and this is helping us to better identify convergent antibodies associated with disease resilience. We are excited to further progress our research and leverage our expertise to develop pioneering ways of treating diseases in the field of immunotherapy.”

The study found that the B cell receptor (BCR) sequence representations separate according to mutational load and the underlying BCR V gene segments used. Importantly, there is distinct partitioning of BCRs derived from naïve versus memory B cells, suggesting that functionally important information is captured by the model. Finally, the model recognized pairs of positions within the BCR sequence that form contacts in three-dimensional space. These data demonstrate that AntiBERTa learns various characteristics of the BCRs, such as B cell origin, activation level, immunogenicity, and structure.

Dr. Jane Osbourn, PhD, Co-founder and Chief Scientific Officer of Alchemab, commented: “Our AntiBERTa technology has the potential to transform our ability to understand antibody structure and function and will inform our understanding of antibody paratopes, or the amino acid sequences comprising the site at which antibodies bind to antigens. It will also enable Alchemab to continue to build its unbiased platform to identify novel oncology and neurodegenerative targets. Alchemab’s novel approach learns from nature and naturally optimized antibodies and works backwards to uncover the most important targets and pathways involved in disease modulation. This approach has been very successful, leading to the identification of several novel oncology and neurodegenerative disease drug targets.”

About Alchemab

Alchemab has developed a highly differentiated platform which enables the identification of novel drug targets, therapeutics and patient stratification tools by analysis of patient antibody repertoires. The platform uses well-defined patient samples, deep B cell sequencing and computational analysis to identify convergent protective antibody responses among individuals that are susceptible but resilient to specific diseases.

Alchemab is building a broad pipeline of protective therapeutics for hard-to-treat diseases, with an initial focus on neurodegenerative conditions and oncology. The highly specialized patient samples that power Alchemab’s platform are made available through valued partnerships and collaborations with patient representative groups, biobanks, industry partners and academic institutions.

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Olivia Cavlan

Chief Corporate Development and Strategy Officer