BigHat Biosciences
AI-driven antibody engineering with closed-loop lab validation
BigHat Biosciences combines machine learning with high-throughput wet lab experiments in a closed-loop system to design safer, more effective antibodies. The company's platform iterates between computational prediction and experimental validation, building proprietary datasets that continuously improve its AI models for antibody optimization.
Antibody Design, Platform
Funding
$148M
Total raised
$75M
Led by Section 32
Section 32 · a16z Bio + Health · AME Cloud Ventures · Byers Capital
Technology
Closed-loop antibody design platform integrating ML models with high-throughput wet lab experiments. The system generates proprietary datasets that iteratively improve predictions of antibody function, safety, and manufacturability.
Leadership
Mark DePristo
CEO & Co-Founder
Former Google Brain, Broad Institute; led GATK development
Peyton Greenside
CTO & Co-Founder
PhD from Stanford, computational genomics
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