AbSci and Other AI-Powered Biotechs Lead the Way in Antibody Discovery

by Andrii Buvailo, PhD          Biopharma insight

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The monoclonal antibodies (mAbs) market has seen impressive growth in recent years, with the top-selling drugs increasingly being mAbs. The success of these therapeutics has driven the pharma industry to seek innovative methods for refining and improving their antibody pipelines. Artificial intelligence (AI) and deep learning, which have already revolutionized small molecule drug design, are now making significant strides in the development and optimization of antibodies.

Traditional mAb discovery involves experimental techniques like hybridoma screening or high throughput platforms such as yeast or phage display, followed by computationally guided mutagenesis or evolution of the antibody sequence. AI companies have made substantial progress in optimizing antibodies for binding, solubility, yield, and immunogenicity, among other properties. Some companies are even working on de novo design of antibody candidates, bypassing time-consuming and costly experimental methods.

 

Zero-shot moonshot

Absci has achieved a significant milestone in generative AI drug development: they are the first to design and validate de novo therapeutic antibodies using a zero-shot generative AI approach. Zero-shot methods involve designing antibodies that bind to specific targets without relying on any training data from known antibodies binding to those particular targets.

Zero-shot learning in the artificial intelligence field refers to a learning method where a model is able to recognize and classify new, previously unseen objects or data without having any prior examples or training data related to those specific objects. This is achieved by leveraging the model's existing knowledge and understanding of related objects or concepts and applying it to the new, unseen data.

In traditional machine learning approaches, a model is trained on a set of examples for each class or object it needs to recognize. In zero-shot learning, however, the model uses its existing knowledge base, often in the form of semantic relationships or shared attributes between known and unknown classes, to make predictions about the new, unseen data.

AbSci started its journey in 2011 in the city of Vancouver, Washington, US, and a decade later went public having raised about $200 million in an IPO in 2021. AbSci aims to change the biopharmaceutical industry with their protein production platform. Their advanced technology enables the fast and cost-effective production of complex proteins, including monoclonal antibodies, which helps create new treatments.

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Topics: Biotech Companies   

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