Iambic and Takeda have announced a multi-year technology and discovery collaboration agreement to advance the AI-driven design and development of high-priority small molecule drug programs, initially focused on oncology and gastrointestinal and inflammation therapeutic areas, under which Takeda will leverage Iambic’s industry-leading AI drug discovery models and integrated automated wet lab capabilities to accelerate candidate design, make, test and analyze cycles and gain access to NeuralPLexer, Iambic’s proprietary model for predicting protein-ligand complexes that can shorten discovery timelines and improve candidate selection; under the terms of the agreement, Iambic will receive upfront, research cost and technology access payments and is eligible for success-based payments that could exceed $1.7 billion, as well as royalties on net sales of products arising from the collaboration, marking one of the largest AI collaborations in pharmaceutical discovery to date and reflecting the growing industry shift toward integrating cutting-edge computational platforms with traditional drug development processes to boost efficiency and outcomes while addressing critical unmet patient needs such as cancer and inflammatory disease.
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“Our collaboration with Takeda is a powerful opportunity to apply our AI-driven discovery and development platform, and we are excited to partner with their team to quickly advance new and better drug candidates,” said Tom Miller, Ph.D., Co-Founder and CEO of Iambic, underscoring how the partnership validates Iambic’s technology and highlights the breadth and scale of its discovery capabilities, and “We are excited to be able access Iambic’s proprietary computational platform while we work with their team to develop small molecule therapeutics with the potential to address critical unmet patient needs,” said Chris Arendt, Ph.D., Chief Scientific Officer and Head of Research at Takeda, noting Takeda’s strategy of accelerating impactful new medicines by leveraging advanced science including artificial intelligence as the collaboration aims to de-risk candidate selection, improve probability of success, and more rapidly advance select programs from early project start to IND.
