UNITED STATES OF AMERICA —Venture capital firms DFJ Growth and Premji Invest have co-led a $75 million Series C investment round in BigHat Biosciences, bringing the biopharmaceutical startup's total funding to $223 million. Additional backing came from institutional investors including Catalio Capital Management, LG Technology Ventures, Sigmas Group, 8VC, Andreessen Horowitz, and strategic corporate venture arms from pharmaceutical leaders such as Amgen, Eli Lilly, and Merck. Headquartered in San Mateo, California, BigHat Biosciences is a clinical-stage biotechnology firm that merges machine learning algorithms with automated laboratory experimentation to engineer next-generation antibody therapeutics.
The capital injection will primarily fund clinical trials for BHB810, an antibody-drug conjugate directed against CDH17 for treating gastrointestinal cancers, alongside preclinical development for BHB299, a T-cell engager targeting solid tumors. Beyond internal pipeline development, the expansion supports the scale-up of BigHat's proprietary biological platform, which generates targeted experimental datasets to train reinforcement learning models for antibody engineering. The approach addresses a primary bottleneck in biologic drug development by optimizing pharmacokinetics and therapeutic properties prior to human testing.
This financing underscores a broader industry shift toward AI-native biotechnology platforms capable of generating wet-lab data in closed-loop systems. Biopharmaceutical companies increasingly rely on automated experimentation to reduce trial failure rates and accelerate pipeline advancement, establishing machine learning as a core component of early-stage therapeutic discovery. Venture capital deployment into platform-driven drug discovery indicates persistent institutional confidence in computational biology tools that demonstrate clinical validation.
For global pharmaceutical manufacturers and biotech investors, the capital deployment reflects growing demand for strategic co-development partnerships between technology developers and traditional life sciences companies. As platform capabilities advance toward human trials, life science organizations that integrate automated data generation with generative models gain a distinct operational advantage in reducing development timelines for complex biologics.
