Takeda Pharmaceuticals has entered into a strategic partnership with Hong Kong-based Insilico Medicine to leverage AI in early-stage drug discovery across the Japanese pharmaceutical company’s therapeutic areas.
The companies have not disclosed the therapeutic areas or diseases targeted by the collaboration. The agreement provides Takeda with access to in silico’s Pharma.AI platform, which supports biological target identification, molecular design, and clinical trial prediction.
The companies said the collaboration will focus on identifying drug candidates that meet pre-defined scientific and early development criteria. Insilico will lead the AI-driven discovery effort and Takeda will be responsible for advancing selected candidates through clinical development.
Transaction value and development rights
Takeda will receive exclusive worldwide rights to develop, manufacture and commercialize selected novel therapeutics through the collaboration.
Insilico said the deal includes approximately $60 million in project initiation fees, short-term payments and milestones. Total proceeds could reach approximately US$600 million if preclinical, clinical, commercial and sales milestones are achieved.
Additional payments will be tied to preclinical, clinical, commercial and sales milestones. Insilico is also eligible to receive tiered royalties on future sales.
Insilico founder and CEO Alex Zaboronkov said proceeds from the deal will support early-stage research and development under the cooperative program. Zaboronkov also said that the late-stage schedule will depend on Takeda’s clinical development activities and the companies’ collaborative efforts.
AI drug discovery partnership
The Pharma.AI suite includes tools used for target discovery, molecule generation, and clinical development prediction. Published descriptions of the platforms include PandaOmics for target discovery, Chemistry42 for novel small molecule generation, and InClinico for predicting clinical trial transition probability.
Insilico has also advanced its own AI-generated drug candidates into clinical trials. Lensertib, formerly known as ISM001-055 or INS018_055, is a small molecule TNIK inhibitor for idiopathic pulmonary fibrosis that was evaluated in a phase 2a randomized clinical trial.
Chris Arendt, Takeda’s chief scientific officer and head of research, said the agreement combines Takeda’s disease biology research with Insilico’s AI-powered discovery capabilities. He said Takeda is also integrating automation, robotics and generative AI into its discovery work.
The in silico deal follows another AI drug discovery deal with Takeda earlier this year. In February, Takeda entered into a multi-year partnership worth more than $1.7 billion with Iambic to leverage AI in the design of small molecule drugs for cancer and gastrointestinal diseases.
Iambic’s platform includes NeuralPLexer, an AI model used to predict how drug molecules bind to proteins.
Chinese pharmaceutical companies signed 157 out-licensing deals worth US$135.7 billion in 2025, according to data cited by the South China Morning Post from China’s State Administration of Pharmaceutical Products.
Under Takeda and Insilico’s agreement, Takeda will receive exclusive worldwide rights to candidates discovered through Insilico’s platform. Insilico said it has signed collaboration agreements since the beginning of the year that could total more than $7 billion.
Last month, Insilico announced a partnership with South Korea’s SK Biopharmaceuticals focused on neuroimmune diseases. The agreement includes up to US$18 million in upfront and short-term milestone payments, with a potential total value of more than US$2.5 billion.
In March, Eli Lilly expanded its collaboration with Insilico with an AI-powered drug discovery agreement worth up to US$2.75 billion. The agreement gives Lilly exclusive worldwide rights to develop, manufacture and commercialize certain oral therapeutics in preclinical development.
Following the announcement of the agreement with Takeda, Insilico’s Hong Kong-listed shares rose 13.5%.
(Photo provided by Serkan Yildiz)
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