Logic-Gated T Cell Engagers
First-in-class antibodies engineered to know when NOT to attack
Company profile
Company profile
BigHat Biosciences is a Series B biotechnology company that develops next-generation antibody therapeutics using an integrated machine learning and synthetic biology platform. Founded in 2019 and headquartered in San Mateo, California, the company has raised over $100 million from top investors to pursue its mission of creating safer, more effective antibody therapies for patients. The company's proprietary Milliner platform combines a synthetic biology-based high-speed wet lab with state-of-the-art machine learning technologies into a full-stack antibody discovery and engineering system. At the heart of this platform is Reccy Antibody Design Studio (RADS), an AI-driven system that orchestrates thousands of models and datasets through continuous design-build-test cycles. The platform can synthesize, purify, and fully characterize hundreds of recombinant antibodies for biophysics and function in a single weekly workcell, dramatically accelerating the path from hypothesis to development candidate. BigHat's technology addresses the complex challenges of antibody therapeutic development, including affinity, specificity, stability, manufacturability, and immunogenicity. The company applies its capabilities to both wholly-owned therapeutic programs and strategic partnerships with leading pharmaceutical companies including Eli Lilly, Johnson & Johnson, and Lonza's Synaffix. By leveraging cell-free protein synthesis, automated assays, and iterative machine learning optimization, BigHat is advancing treatments for today's most challenging diseases, from inflammation and infections to cancers.
Company description BigHat Biosciences official website
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01 · Operating footprint
Company-profile context for clinical focus, customers, deployment, integration, milestones, and partnerships.
On Premise · Other deployment with recorded integration context.
Company milestones
Appointed Stefan Weigand as Chief Scientific Officer (June 2026); Launched Reccy Antibody Design Studio/RADS (September 2025); Partnership with Lilly for ML-enabled biologics discovery (January 2026, April 2025); Completed J&J strategic collaboration (October 2025); Lonza Synaffix ADC collaboration (November 2024)
Partnerships
02 · Flagship portfolio
Selected source-backed Company products and the available commercial context.
First-in-class antibodies engineered to know when NOT to attack
AI-driven antibody discovery and engineering platform integrating ML with high-throughput wet lab
Fully integrated AI platform orchestrating ML models and datasets for antibody optimization
03 · Research intelligence
Publications, preprints, clinical validation, and regulatory records in Evidence Position order.
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Research
Journal of medical Internet research · Jul 11, 2025
Molecular subtyping of hypertensive disorders of pregnancy.Nature communications · Apr 8, 2025
Using deep learning to annotate the protein universe.Nature biotechnology · Jun 1, 2022
RNA profiles reveal signatures of future health and disease in pregnancy.Nature · Jan 1, 2022
Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution.Cell · Sep 30, 2021
Challenges of Accuracy in Germline Clinical Sequencing Data.JAMA · Jul 20, 2021
Clinical validation
NCT07529808 · Sponsor · RECRUITING
Regulatory
No linked records are currently available.
04 · Company-reported outcomes
Company-reported outcomes and customer stories are distinct from linked Research Intelligence evidence.
Single weekly workcell throughput
Since founding in 2019 across hundreds of design rounds
Advance machine learning-enabled biologics discovery through Lilly TuneLab and AI-driven antibody therapeutics development
Strategic collaboration for antibody therapeutic development
Successful completion of strategic collaboration
Development of machine learning-designed antibody-drug conjugate (ADC)
05 · Leadership
Leadership profiles and professional links from the current Company record.
06 · Company updates
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07 · HAIC coverage
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08 · Official presence
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09 · Market pathway
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Market context
Companies using AI, machine learning, or computational biology to discover new drug candidates, identify drug targets, predict drug efficacy, or accelerate pharmaceutical development. Includes both small molecule and biologics development, precision oncology drug discovery, and AI-driven target identification and molecule design.
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