Nabla Bio, a biotechnology company using generative AI to design novel therapeutics, announced a major expansion of its partnership with global pharmaceutical company Takeda. The new multi-year collaboration could be worth over $1 billion in total, marking one of the most significant AI-driven drug discovery deals to date.
Under the terms of the agreement, Nabla Bio will receive double-digit millions in upfront payments and research funding. The company is also eligible for success-based milestone payments that could push the total value beyond the billion-dollar mark.
This is the second collaboration between the two companies. It will apply Nabla Bio’s proprietary Joint Atomic Model (JAM) platform across Takeda’s early-stage drug development efforts. The focus includes de novo antibody design across multiple targets, multispecifics, challenging disease targets, and custom biologic therapies.
“Since 2022, we’ve worked with Takeda to push the boundaries of next-gen biologics,” said Surge Biswas, Ph.D., CEO and Co-founder of Nabla Bio. “This expanded partnership reflects our shared belief that AI-powered de novo design can unlock new therapeutic frontiers and dramatically speed up drug discovery.”
Takeda, which has been investing heavily in artificial intelligence to accelerate R&D, echoed the sentiment. “This collaboration applies Nabla’s cutting-edge AI and wet lab integration to help us design and optimize protein therapeutics across our focus areas,” said Chris Arendt, Ph.D., Chief Scientific Officer and Head of Research at Takeda.
Nabla Bio’s JAM platform combines large-scale generative AI with direct biological testing. It has already shown promising results, including high success rates in designing functional antibodies for difficult targets like GPCRs and delivering complex biologics with strong preclinical profiles.
The partnership highlights a growing trend in pharma, as AI-native biotech companies like Nabla Bio continue to demonstrate the potential of machine learning to transform drug development from early design to preclinical validation.