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AI Revolutionizes Drug Discovery: Amgen Leads the Charge

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  Published in Science and Technology on Tuesday, February 10th 2026 at 13:22 GMT by The Financial Times
      Locales: UNITED STATES, UNITED KINGDOM

Thousand Oaks, California - February 10th, 2026 - The pharmaceutical industry stands on the precipice of a monumental transformation, fueled by the rapid advancements in artificial intelligence. Kim Jackson, Chief Scientific Officer of Amgen, a global biotechnology leader, believes AI is not just enhancing drug discovery, but fundamentally reshaping it - by directly addressing the sector's most persistent bottleneck: identifying and validating promising drug targets.

Speaking exclusively to this publication from Amgen's California headquarters, Jackson articulated a vision where AI acts as a powerful extension of human scientific expertise. "For decades, the biggest challenge in bringing new medicines to patients has been pinpointing the right targets - the specific molecules or pathways within the body that, when modulated, can alleviate disease," she explained. "If we can reliably and efficiently solve that problem, a cascade of downstream improvements in the entire drug development process follows."

Amgen's commitment to AI isn't simply a technological upgrade; it's a strategic overhaul. The company has invested heavily in both the computational infrastructure - the hardware and software necessary to power AI algorithms - and, crucially, in recruiting and training the highly skilled personnel needed to harness its potential. Jackson, appointed CSO in 2021, has spearheaded this initiative, recognizing AI as pivotal to Amgen's future R&D success.

"The narrative isn't about robots replacing researchers," Jackson emphasized. "It's about empowering them. AI provides an unprecedented level of computational power, allowing us to explore a far wider landscape of potential drug targets, and to do so at an exponentially faster rate than previously possible."

From Decade-Long Pipelines to Accelerated Timelines

The traditional drug discovery process is notoriously lengthy and expensive. Historically, it's taken an average of 10-15 years and upwards of $2.5 billion to bring a single new drug to market. The vast majority of potential candidates fail along the way, representing a colossal loss of investment and, more importantly, delayed access to potentially life-saving treatments. AI promises to drastically alter this landscape.

By leveraging machine learning algorithms to analyze vast datasets - including genomic information, clinical trial results, and scientific literature - AI can predict which targets are most likely to be successful, dramatically reducing the number of dead ends and accelerating the overall timeline. Amgen's strategy involves a dual approach: developing proprietary AI models tailored to their specific needs, and forging strategic partnerships with specialized AI companies.

"We're heavily focused on creating 'foundation models'," Jackson revealed. "These are large, pre-trained AI models that serve as a versatile base, capable of being fine-tuned for a diverse range of specific tasks within drug discovery. It's akin to having a highly adaptable core technology that can be applied to multiple problems."

Navigating the Challenges: Data Integrity and 'Explainable AI'

Despite the immense potential, Jackson acknowledges that integrating AI into drug discovery isn't without its hurdles. Data quality remains a paramount concern. AI models are only as effective as the data they're trained on; biased, incomplete, or inaccurate data can lead to flawed predictions. Amgen is prioritizing rigorous data curation and validation processes to ensure the reliability of their AI-driven insights.

Another key challenge is interpretability. Many advanced AI models, particularly deep learning networks, operate as "black boxes," making it difficult to understand how they arrived at a particular conclusion. This lack of transparency is unacceptable in a field as critical as drug development.

"We don't just need to know what the AI recommends; we need to understand why," Jackson stressed. "Being able to explain the rationale behind a model's predictions is crucial for building trust with scientists, convincing regulatory agencies like the FDA, and ultimately ensuring patient safety."

The Future of R&D: Seamless AI Integration

The final piece of the puzzle lies in seamlessly integrating AI-generated insights into existing research and development workflows. Amgen is actively developing new tools and processes to facilitate this integration, making it easier for scientists to access, interpret, and utilize AI's recommendations in their daily work. This includes creating intuitive interfaces and visualization tools that allow researchers to explore complex datasets and understand the underlying logic of AI predictions.

Jackson envisions a future where AI is an indispensable part of the drug discovery process, accelerating the pace of innovation and ultimately delivering more effective treatments to patients in need. "We're still in the early stages of this revolution," she concluded, "but the potential is truly transformative. AI isn't just changing how we do drug discovery; it's changing what's possible."