10 Companies Applying AI for Biologics Discovery

At the same time, the number of companies using AI for biologics discovery is also continuously growing. In BiopharmaTrend report there are already 65 selected AI-driven companies focusing on biologics. 

According to the definition by FDA, biological drugs include vaccines, blood and its components, somatic cells, gene therapy, recombinant therapeutic proteins and even tissues. Some narrower examples include therapeutic antibodies for cancer treatment or even COVID-19 vaccines, but all biologics share the same composition “requirement” (sugars, proteins, nucleic acids, their combinations or living entities) and the isolation origin (humans, animals, plants, microorganisms).

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Below we review ten notable companies applying AI for designing biologics drugs in more detail.

AbCellera Biologics 

Canadian company AbCellera, a biotech company that develops antibody therapeutics, was founded in 2012 and closed the IPO round in the end of 2020 with $555.5M gross proceeds. 

AbCellera aims to search, decode, and analyze natural immune systems to find therapy-suitable antibodies, as well as use humanized antibody platforms to access and assess various functional modalities of the potential therapeutic molecules.

They own sequencing technologies with functional data from single B-cells, which enables researchers to increase the number of antibody candidates. Additionally, their proprietary antibody visualization software Celium helps to deal with the obtained multidimensional data.

After selecting the most promising leads by the number of various criteria, computational protein engineering can be used to optimize antibodies, while also considering the natural antibodies diversity.

Asimov 

Founded in 2017, Asimov is a US-based company building tools to program living cells. Their total funding reached $30M from a number of investors,summing up three funding rounds. 

Asimov owns a mammalian cell engineering platform, which can help to accelerate the design and manufacturing for biologics and gene therapies. First, computer-aided design is used along with the simulation and debugging of genetic systems. 

At the same time, ML bridges large-scale datasets with mechanistic models of biology. In general, AI assists with designing and understanding biological complexity.