November 29, 2022
Collaboration leverages BigHat’s AI-enabled platform to expedite protein engineering to design and develop novel therapeutic candidates
SAN MATEO, CA, November 29, 2022 — BigHat Biosciences, a biotechnology company with a machine learning-guided antibody discovery and development platform, today announced a collaboration with Merck, known as MSD outside of the United States and Canada, to apply the company’s technology to design candidates for up to three drug discovery programs.
BigHat’s design platform, Milliner, integrates a high-speed characterization with ML technologies to engineer antibodies with more complex functions and better biophysical properties. This approach could help reduce the difficulty of optimizing antibodies and other therapeutic proteins.
Under the collaboration, BigHat and Merck will collaborate to optimize up to three proteins by leveraging BigHat’s platform to synthesize, express, purify, and characterize molecules. Mark DePristo, co-founder and CEO of BigHat, continued, "We are thrilled to be collaborating with Merck's world-class drug development teams to design safer, more effective antibodies for these important therapeutic programs.”
The teams have initiated work on the first program and are looking forward to using the power of the complementary skills sets within each research team to generate high-quality lead antibodies. “We are excited to begin this collaboration to advance next-generation antibody therapeutics to patients,” said Elizabeth Schwarzbach, BigHat’s Chief Business Officer. "This agreement with Merck brings us a major step closer to our goal of 3-5 deep collaborations with leading biopharmas to complement our internal therapeutic pipeline."
“This agreement with BigHat expands Merck’s strategy of applying AI/ML across our drug discovery capabilities,” said Juan Alvarez, vice president of Biologics Discovery, Merck Research Laboratories. “We look forward to working with the team to leverage BigHat’s technology and expertise in enabling molecular design of novel biologic candidates.”