Chinese Innovative Biopharma Firms Embrace AI as AI-Driven Drug Discovery Race Accelerates

Beijing, June 21 (CNS) – By 2026, AI-driven drug development has evolved from a question of “whether to do it” into a new race of “who can move faster.”

Recently, Harbour BioMed and BioMap jointly announced the establishment of MegaStream TechBio, a new AI-driven R&D company targeting the global market. The new venture will integrate BioMap’s xTrimo, a full-modality large life science model with 268 billion parameters, while leveraging Harbour BioMed’s extensive high-quality data assets accumulated over years in the field of global fully human antibodies.

“This is an upgrade from project-based to platform-to-platform collaboration,” pointed out a securities analyst who has long followed AI-driven drug development. Previously, most “AI + biotech” collaborations were limited to service procurement at the project level. In contrast, this partnership is designed from the top down at the corporate level, deeply integrating industry, academia, research, computing power, and data, thereby elevating competitive barriers to the ecosystem level.

Not only Harbour BioMed, but the innovative drug industry has also significantly accelerated its collaborative layout around AI-driven drug development this year.

In January, CSPC and AstraZeneca formed a strategic partnership to develop long-acting drugs leveraging their sustained-release drug delivery and peptide AI discovery platforms; in March, Roche deployed 2,176 high-performance GPUs globally to expand its AI infrastructure; in the same month, Eli Lilly launched its AI factory, LillyPod;Fosun PharmaceuticalWe will continue to advance the “Full Embrace of AI” strategy, accelerating the implementation of AI in R&D and industrial scenarios.

“The collective embrace of AI by the innovative drug industry is not a fleeting trend, but rather stems from an increasing number of enterprises recognizing the tangible value AI brings to new drug R&D practices,” said Wang Jinsong, Founder, Chairman, and CEO of Harbour BioMed, in an interview with reporters. In his view, AI’s role in new drug development is evolving from an “auxiliary tool” to a “core engine,” gradually integrating into the entire drug R&D process rather than remaining confined to localized applications. It is not only serving to enhance efficiency but also beginning to participate in project design and R&D decision-making, while extending into the clinical translation phase.

Wang Jinsong further pointed out that although companies follow different paths, the common trend is a shift from “external add-on tools” to “core internal support.” “If the past was about moving from the outside in, the present is about radiating from the inside out.”

Leading AI companies and major internet tech giants have also sprung into action.

In April, OpenAI announced the launch of GPT-Rosalind, a reasoning model designed for drug discovery and translational medicine. The initial customer list included Amgen, Moderna, and the Allen Institute. In June, ByteDance initiated the spin-off and independent fundraising of its AI-driven drug discovery business, transferring its core team, algorithm platform, and pipeline assets to a new entity. ByteDance will retain controlling interest, while Volcano Engine will continue to provide computing power support. With this move, tech giants such as ByteDance, Baidu, Huawei, and Tencent have all entered the AI drug discovery sector through various approaches.

Confidence in the capital market has also been significantly restored.PHARSCIN PHARMAEarendil Labs, an overseas AI platform, completed a $787 million financing round in late March; Deep Intelligent Pharma, an AI healthcare company, cumulatively secured $150 million in financing within just three months.

Yet beneath the clamor, hidden concerns are also emerging. The stages that AI can currently optimize still account for a small proportion of the lengthy new drug development pipeline; data silos remain difficult to break down, and the logical frameworks of algorithms have not yet truly interoperated with the language system of life sciences. More troublingly, the industry is even more mixed in quality than it was two years ago. Industry insiders revealed to reporters that many pharmaceutical companies have lowered their criteria for selecting AI partners from “discovering and validating revolutionary targets” to merely “being reliable.”

Yet the core question posed by investors and the secondary market remains unchanged: Can molecules designed by AI ultimately become new drugs approved for commercial sale? As AI-driven pharmaceutical companies such as Insilico Medicine, XtalPi, and Metagenomi sequentially enter the capital markets, the industry has begun to measure the true value of AI in drug discovery using the rigorous standards of the pharmaceutical sector. After the hype subsides, only those who can deliver results that withstand clinical validation will go further in this race. (End)