BigHat Biosciences utilizes a machine learning-guided antibody design platform, Milliner, which integrates synthetic biology and high-speed wet lab processes to rapidly synthesize and characterize recombinant antibodies. The company focuses on developing safer and more effective antibody therapies for patients with challenging diseases, enhancing treatment options in the biopharmaceutical landscape.
Funding
$139.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
S3Founders
Product
Problem
Developing effective antibody therapies is a slow, expensive, and inefficient process, often hindered by the limitations of traditional antibody discovery and engineering methods. Existing approaches struggle to fully explore the sequence space and accurately predict developability and safety profiles early in the development pipeline. This results in increased clinical failure rates and delays in bringing life-saving treatments to patients.
Solution
BigHat Biosciences offers Milliner, a machine learning-guided antibody design platform that accelerates the discovery and optimization of therapeutic antibodies. Milliner integrates high-throughput synthetic biology with advanced machine learning algorithms to guide the design, synthesis, and characterization of recombinant antibodies. The platform enables rapid exploration of antibody sequence space, predicting developability and safety profiles to identify promising candidates with enhanced efficacy and reduced immunogenicity. By combining in silico design with high-speed wet lab validation, BigHat significantly reduces the time and cost associated with traditional antibody development, leading to safer and more effective antibody therapies.
Target Audience
BigHat's primary customers are biopharmaceutical companies and research institutions seeking to accelerate antibody discovery, improve antibody developability, and develop novel antibody therapeutics for challenging diseases.
Features
- Machine learning-driven antibody design optimizing for developability, safety, and efficacy
- High-throughput antibody synthesis and characterization using synthetic biology
- Biophysical and functional characterization of hundreds of recombinant antibodies per week
- Advanced algorithms for predicting developability issues such as aggregation and immunogenicity
- Full-stack antibody discovery and engineering platform integrating computational design and experimental validation