Private company Iambic announced on Monday that it has entered into a multi-year collaboration with Japan's Takeda Pharmaceutical Co Ltd, valued at over $1.7 billion. The partnership will leverage artificial intelligence technology to aid in the development of small-molecule drugs targeting cancer and gastrointestinal diseases.

Under the agreement, Iambic will receive an upfront payment and is eligible to receive more than $1.7 billion in potential payments tied to development and commercial milestones, in addition to royalties on future drug sales.

This collaboration marks another significant step by Takeda Pharmaceutical Co Ltd to integrate artificial intelligence across the entire drug discovery pipeline, following a similar partnership last year with Nabla Bio that focused on protein-based therapeutics.

Pharmaceutical companies are increasingly utilizing artificial intelligence technologies to accelerate drug development and reduce research costs. Industry experts predict that the overall drug discovery timeline could be cut in half within the coming years.

As part of the collaboration, Takeda Pharmaceutical Co Ltd will also gain access to Iambic's NeuralPLexer model, which can predict how drug molecules bind to proteins.

Iambic's Chief Executive Officer Tom Miller explained that deciphering protein structures is a critical step in the drug discovery process. He stated, "If you don't know the structure of the target you want to bind to, developing a drug is like trying to sculpt a statue in the dark."

In traditional drug development, moving a compound from discovery to clinical trials typically takes approximately six years. Iambic claims that its approach, which combines AI prediction technology with automated laboratories, can compress this timeline to under two years.

Christopher Arendt, Chief Scientific Officer at Takeda Pharmaceutical Co Ltd, emphasized that while the technology significantly shortens development cycles, improved efficiency represents only part of its value. "Integrating an artificial intelligence engine into small-molecule drug discovery not only accelerates the pace of research," Arendt noted in an interview, adding that the quality of the resulting drug molecules is equally crucial.

Miller further highlighted that AI tools can save months of traditional laboratory work, with their core value lying in "achieving research outcomes that were previously impossible with existing technologies."