Nabla Bio and Takeda expand AI-powered drug design partnership in multi-year, billion-dollar deal

IMT News Desk
IMT News Desk
· 2 min read
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U.S. biotechnology firm Nabla Bio has signed a major new research partnership with Japan’s Takeda Pharmaceutical, deepening the two companies’ collaboration on artificial intelligence-driven drug discovery. Announced on Tuesday, October 14, the multi-year agreement is built on a previous collaboration begun in 2022, and positions Nabla Bio to receive upfront and research payments in the double-digit millions, with the potential for more than $1 billion in success-based payments.

The partnership underscores the growing momentum across the global pharmaceutical sector to harness artificial intelligence in drug development, aiming to streamline timelines and cut costs for complex new medicines. Nabla Bio will deploy its proprietary Joint Atomic Model (JAM) platform—an advanced AI technology—to design novel protein-based therapeutics tailored for Takeda’s early-stage drug pipeline. The focus will be on addressing hard-to-treat diseases, including the development of multi-specific drugs and other custom biologics.

Analogous to how platforms like ChatGPT answer text questions, Nabla’s JAM responds to molecular queries by designing antibodies from scratch, targeting specific disease properties. Nabla Bio claims it maintains possibly the fastest feedback loop in the industry, with a turnaround from design to laboratory testing in just three to four weeks. CEO Surge Biswas told Reuters, “We are basically working on whatever the most pressing problems in Takeda’s discovery portfolio is at any given time, and using JAM to help unlock and unblock those.”

Takeda’s renewed commitment to AI is reflected in its recent exit from cell therapy research to prioritize faster, more scalable approaches. The company also joined a consortium with Bristol Myers Squibb to train AI models on shared data. Nabla Bio expects that its first AI-designed molecules could produce human data within one to two years, marking a possible breakthrough in AI-powered drug development.

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