Which Company's Case Study?
This case study focuses on Amgen, one of the world's leading biopharmaceutical companies. Amgen is known for its treatments for diseases such as cancer, severe arthritis, and anemia, and is a pioneer in biological drugs (biopharmaceuticals). Addressing the industry-wide challenge of complex, time-consuming, and costly new drug development processes, Amgen is pioneering innovation through the use of generative AI.
Challenges Amgen Aimed to Solve
Traditional drug discovery, especially the development of biologics, is a highly costly and time-consuming process, requiring the screening of tens of thousands to millions of molecules to find effective candidates. Furthermore, even after discovering promising proteins (such as antibodies), many cases failed to reach commercialization due to challenges like manufacturability and stability. Amgen aimed to accelerate and streamline this long and uncertain process using AI.
How AI Was Used
Amgen is using NVIDIA technology to build generative-biology models. AI designs protein-based molecules intended to interact with disease targets and runs millions of simulations to identify candidates for further real-world testing.
Implementation Effects and Key Takeaways
- ▸Amgen installed a DGX SuperPOD named Freyja at deCODE genetics.
- ▸The system supports analysis of about 200 petabytes of deidentified human data.
- ▸Unverified claims that a particular design became a drug candidate or cut development time by a stated amount are omitted.
What Japanese Companies Can Learn
This case study demonstrates that generative AI can be a powerful tool not only in the pharmaceutical industry but also in highly specialized R&D fields. The key is a company's unique datasets accumulated over many years. By training these datasets on the latest AI platforms, it is possible to solve complex industry-specific challenges and create innovations that cannot be replicated by others. The approach of fine-tuning models with proprietary data, rather than just using general-purpose AI, can be applied in many areas, such as material development in manufacturing and risk model construction in the financial industry.