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Huawei widens pharma hunt in AI race for new drugs

Technology conglomerate Huawei plans to expand its artificial intelligence cooperation with mainland pharmaceutical firms into drug development and clinical practice, a senior executive said. The move highlights Huawei's ambitions to gain a foothold in the fast-growing AI drug discovery market, where pharmaceutical companies are investing in modelling tools and automated laboratories to shorten…

Huawei, the technology conglomerate, is broadening its artificial intelligence collaboration with mainland pharmaceutical companies into drug development and clinical practice, according to a senior executive. This expansion underscores Huawei's strategic intention to establish a presence in the rapidly expanding AI drug discovery market, where pharmaceutical entities are investing in modeling tools and automated laboratories to expedite development timelines and increase efficiency.

Speaking on Wednesday, William Zhang, president of Huawei's healthcare business unit, emphasized that further exploration of AI in the medical field will lead to increased collaboration and fruitful outcomes with pharmaceutical companies, encompassing drug manufacturing, clinical trials, and final implementation. Huawei has already established collaborations in clinical practice within hospitals and is actively seeking additional opportunities.

The current projects primarily involve domestic drugmakers, he added. Major US chip manufacturer Nvidia has also formed AI-related partnerships with drug companies like Eli Lilly and Novo Nordisk, as technology firms strive to capitalize on the rising demand for AI-powered drug research. Huawei provides tools for identifying potentially viable drug compounds, complemented by its Ascend and Kunpeng chips.

In May, Huawei announced a project in collaboration with state-owned Guangzhou Pharmaceutical Holdings, marking the industry's first production validation of independently developed AI drug research models adapted to its Ascend and Kunpeng technologies. Forecasts suggest that the application of machine learning to optimize target discovery, design molecules, and streamline clinical trial planning could reduce early-stage development timelines and costs by half within the next three to five years.

Written by urgent.news from RTHK News - Finance's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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