This Protein Therapeutics Company Integrates Wet Lab For High-Speed Characterization With Machine Learning Technologies To Guide The Search For Better Antibodies

BigHat Biosciences is a protein therapeutics startup based in San Carlos, California. It is the developer of AI-guided antibody designs that aim to synthesize the high-speed antibody characterization with artificial intelligence that would lead to disease-fighting molecules’ production. In the recent funding rounds, this startup raised around $19 million in Series A funding, taking this organization’s total allocation a little above $24.3 million. BigHat Biosciences’ focus is now aligned towards further refining the antibody production procedure to engineer better disease-fighting molecules. The funding will be put to use by investing a considerable amount into the organization’s R&D wing. 

Biologics is the medication constructed using natural components such as sugar, proteins, DNA, whole cells, and tissues. In the current scenario, there are about 200 biotherapeutics that are being used all across the world. While they offer a good return and generate a revenue of over $100 billion, they are incredibly delicate and take prolonged periods for development. The production of an antibody variant and the characterization of its behavior alone takes up weeks. 

BigHat Biosciences was co-founded in 2019 by Mark DePristo and Peyton Greenside, both extremely qualified individuals. DePristo has, prior to this, served as the head of Genomics for Google AI and has discharged the duties of a director of medical genetics at Harvard and the Broad Institute of MIT. On the other hand, Greenside is a computational biologist at the Broad Institute and has also received her Ph.D. from Stanford for biomedical informatics. The company’s vision is to stimulate the production and identification of antibodies using machine learning.

Using machine learning and AI tools, BigHat has created hundreds of expressed, purified, and characterized antibodies within days in contrast to the traditional methods, which took weeks to complete the same task. Machine learning identifies that a mutation can affect the antibody significantly in terms of its expression, affinity, stability, solubility, and other molecular tendencies; therefore, it assembles a course that takes into account all of these properties and makes an antibody that identifies even the rarest of molecules to fight the disease effectively. 

The proteins manufactured by BigHat have monoclonal antibodies, powering them to bind to multiple targets and offer better tissue penetration. This improves the safety of the antibody and reduces the cost of production at the same time. An apt example of the same is the CAR-T therapy that uses bioengineered antibodies to either stimulate or suppress the immune system, making the body better equipped to fight cancer. 

What makes BigHat unique is that it can pose a hypothesis, conduct real tests in the lab and produce an answer in mere days. Henceforth, BigHat plans to collaborate with more partners to engineer more antibodies and biotherapeutics with machine learning to fight off potential diseases and their variants. 

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Amreen Bawa is a consulting intern at MarktechPost. Along with pursuing BA Hons in Social Sciences from Panjab University, Chandigarh, she is also a keen learner and writer, having special interest in the application and scope of artificial intelligence in various facets of life.

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