Huawei Expands AI Pharma Partnerships Using Domestic Chips Despite Lagging Behind Nvidia

Huawei Announces Plans to Expand AI Pharma Partnerships

Huawei is expanding its AI collaborations with pharmaceutical firms into drug development and clinical practice, according to William Zhang, president of the company's healthcare business unit. The Chinese technology conglomerate aims to establish a foothold in the fast-growing AI-driven drug discovery market, where pharmaceutical companies are investing in modelling tools and automated laboratories to shorten development timelines and improve efficiency1

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. Zhang told Reuters that as Huawei deepens its research into AI in the medical field, more collaboration and results will emerge with pharmaceutical companies from drug manufacturing to clinical to final implementation1

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Focus on Domestic Chinese Drugmakers Using Ascend and Kunpeng Chips

The AI partnerships with drugmakers currently run mainly with domestic Chinese companies, Zhang confirmed2

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. Huawei offers tools for screening potentially viable drug compounds alongside its Ascend and Kunpeng chips, the same domestic stack it has been building since U.S. export restrictions forced the company to find workarounds2

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. Zhang described existing collaborations in clinical practice in hospitals and said the company is exploring more opportunities, though he did not elaborate further3

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. The domestic focus has an obvious constraint behind it, as U.S. export guidance issued last year indicated that using Huawei's Ascend accelerators anywhere is likely to breach American controls, limiting the addressable market for a pitch built on that hardware2

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Limited Track Record Compared to Nvidia's Billion-Dollar Partnerships

The one project Huawei has publicly pointed to is a deal announced in May involving state-owned Guangzhou Pharmaceutical Holdings, which the company described as the industry's first production validation of independently developed AI drug research models adapted to Ascend and Kunpeng technologies1

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. However, the models in question belong to StoneWise, a Beijing AI drug design company that was the third party to the agreement, meaning Huawei's contribution was the silicon and porting work rather than the science itself2

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. The scale gap with competitors is significant. Nvidia has struck AI-related partnerships with drugmakers such as Eli Lilly and Novo Nordisk, with the Lilly arrangement alone being a co-innovation lab with up to $1bn committed jointly over five years2

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. Set against that, Huawei's disclosed pharma work consists of one three-party ecosystem agreement at proof-of-concept stage plus a four-year-old cooperation deal with Yunnan Baiyao dating to 20222

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Huawei's Pangu Drug Molecule Model and Policy Support

Huawei does have a model of its own called the Pangu drug molecule model, released in 2021 and developed with the Chinese Academy of Sciences. The model was trained on 1.7 billion existing compounds to predict how molecules bind to targets2

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. What Huawei has instead of massive partnerships is a policy tailwind. The biopharmaceutical sector was named an emerging pillar industry in this year's government work report, AI in pharmaceuticals is written into the fifteenth five-year plan, and a state-backed body inaugurated in June lists Huawei on the supply side alongside Kingdee and XtalPi2

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. This government support positions Huawei to benefit from China's push for AI-driven pharmaceutical innovation using domestic technology.

Industry Forecasts Suggest Machine Learning Could Transform Drug Development

Industry forecasts suggest that the use of machine learning to optimize target discovery, design molecules and streamline clinical trial planning could halve early-stage development timelines and costs within the next three to five years1

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. Roughly $60bn has gone into AI drug discovery globally, though no AI-discovered drug has yet been approved anywhere. However, 179 candidates were in pipelines by June against four in 2017, and nine have reached Phase III trials2

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. For AI for drug compound screening to deliver on its promise, companies like Huawei will need to demonstrate that their domestically-developed platforms can produce actual drug candidates that advance through clinical trials, not just proof-of-concept validations.