Amgen has cut the time it takes to select promising drug candidates in half by deploying artificial intelligence across its discovery pipeline, manufacturing, and clinical operations. The biotechnology giant's early investment in AI technologies is now delivering measurable results that reshape how the industry approaches one of its most expensive and time-consuming challenges: turning molecular insights into viable medicines.

How Is AI Accelerating Drug Discovery at Scale?

The pharmaceutical industry faces a brutal economic reality: it costs an average of $2.6 billion and over a decade to bring a single drug to market, with roughly 90% of drug candidates failing in clinical trials. Amgen's breakthrough demonstrates how artificial intelligence can compress this timeline and reduce waste at multiple stages of development.

The company has implemented AI tools company-wide, including Microsoft Copilot and OpenAI's ChatGPT Enterprise, alongside comprehensive training programs and responsible-use frameworks. These technologies are designed to be "additive and supportive" to research teams, freeing scientists from routine administrative work so they can focus on higher-value scientific decisions. Beyond these general-purpose tools, Amgen has developed proprietary capabilities including protein folding models and zero-shot antibody design, which uses AI to engineer drug molecules without prior experimental examples.

The operational improvements are striking. At one manufacturing site, AI reduced production line clearance time from approximately 30 minutes to about two minutes per batch run. In clinical trials, AI tools have enabled participant enrollment up to three times more efficiently compared to traditional methods. These gains matter because they directly reduce costs and accelerate the path to patients who need new treatments.

What Role Does Molecular Modelling Play in This Transformation?

Amgen's success reflects a broader industry shift toward molecular modelling, the computational simulation of how drugs interact with biological targets at the atomic level. Molecular modelling is no longer just a scientist's tool; it has become a strategic asset reshaping drug discovery, pipeline prioritization, manufacturing efficiency, and regulatory success.

The convergence of artificial intelligence and molecular modelling is redefining pharmaceutical research and development. A major catalyst has been DeepMind's AlphaFold, which solved the decades-old challenge of predicting protein 3D structures with near-experimental accuracy. This breakthrough has dramatically expanded the universe of druggable targets, enabling researchers to initiate structure-based drug design immediately after target identification and accelerating programs that were previously stalled due to structural uncertainties.

Companies integrating molecular modelling into early-stage discovery report up to 40 to 60% reduction in lead identification timelines and significant cost savings in compound synthesis. Virtual screening can evaluate millions of compounds against a biological target in days, identifying a focused set of high-probability candidates for laboratory validation. This compresses the hit-to-lead phase from months to weeks and slashes reagent, resource, and facility costs dramatically.

How to Leverage AI and Molecular Modelling for Drug Development Success

• Virtual Screening First: Use computational tools to evaluate millions of compounds before conducting expensive laboratory experiments, reducing the number of candidates that require wet-lab validation and accelerating the path to promising leads.
• Predictive Risk Stratification: Employ molecular modelling to identify selectivity risks, metabolic liabilities, drug-drug interaction profiles, and cross-reactivity with off-target proteins early in development, preventing costly failures in later clinical phases.
• Precision Medicine Integration: Analyse patient-specific protein variants and mutations to predict which patient subpopulations will respond to a given drug, enabling more targeted development strategies and stronger regulatory cases.
• Biologics and Next-Generation Modalities: Apply protein-protein interaction modelling, antibody-antigen docking, and RNA secondary structure prediction to accelerate development of monoclonal antibodies, bispecific antibodies, antibody-drug conjugates, and gene therapies.
• Manufacturing Optimization: Use crystal form prediction and computational formulation tools to identify stable crystalline forms of active pharmaceutical ingredients early, reducing formulation development timelines by 20 to 35% and avoiding late-stage polymorph surprises.

Amgen's CEO Bob Bradway has identified 2026 as a critical year for implementing agentic AI systems, which are advanced AI agents designed to automate routine scientific tasks such as form completion, material requisitioning, and data summarization. These systems could free researchers to focus on specialized scientific work that requires human judgment and creativity.

"Scientists today burn hours filling out forms, requisitioning materials, summarizing data, work that agentic systems could absorb entirely, freeing researchers to do what only they can do," Bradway explained, describing this potential as "magic" and emphasizing that AI should be "liberating, rather than displacing" for scientific staff.

Bob Bradway, CEO at Amgen

Why Is Speed in Drug Discovery So Critical Right Now?

The pressure to innovate faster, spend smarter, and outmaneuver competitors has never been more intense. Attrition in Phase II and Phase III clinical trials is where billions disappear, and most failures are rooted in poor target selection, off-target toxicity, or inadequate understanding of mechanism, problems that could have been identified much earlier with robust computational analysis.

Regulatory agencies, including the FDA, EMA, and CDSCO, are increasingly receptive to computational evidence in drug submissions. The FDA's Model-Informed Drug Development guidance and the EMA's push for Physiologically-Based Pharmacokinetic modelling signal a clear directional shift toward accepting in silico evidence. This regulatory openness creates a competitive advantage for companies that invest early in molecular modelling and AI capabilities.

Bradway warned that delayed AI adoption could prove costly for biotechnology companies. "Being late to this party is going to be expensive," he stated. "Being early to the party has the potential for real benefit". Rather than relying solely on external AI models like DeepMind's AlphaFold, Bradway pushed his team to identify specific capabilities and limitations of existing models, then build customized solutions tailored to Amgen's unique research challenges.

Bradway

The traditional drug discovery process has historically relied on iterative laboratory work, manual data review, and sequential testing. AI enables researchers to analyze larger datasets, model molecular behavior, predict protein structures, and evaluate candidate molecules earlier in development, with the goal of deprioritizing less promising candidates sooner in the process. For patients waiting for new treatments, this acceleration means hope arrives faster.