By Kannan Ramachandran, category specialist – cloud computing & data center, and Mathini ilancheran, research manager, R&D, Beroe Inc.
In 2025, pharmaceutical R&D entered a new phase of AI-driven research. Leading companies announced large-scale investments in AI and high-performance computing (HPC) to accelerate drug discovery and advancement. These initiatives are not incremental upgrades,and they represent a basic shift in scientific infrastructure,impacting everything from molecular design to clinical trial execution.
On June 11, 2025, Novo Nordisk partnered with NVIDIA and the Danish Centre for AI Innovation (DCAI) to harness the Gefion sovereign AI supercomputer, powered by NVIDIA DGX SuperPOD technology.1 This platform enables generative and agentic AI models for early-stage research,including molecule design and single-cell simulations.
Just months later, on October 28, 2025, Eli Lilly announced a collaboration with NVIDIA to build the pharma industry’s first in-house AI factory, a DGX-based supercomputer equipped with blackwell Ultra GPUs and running on 100% renewable energy.2 This system is designed for end-to-end AI workflows, from data ingestion and model training to inference, and integrates federated learning through lilly’s TuneLab platform, enabling collaborative innovation with biotech partners.
Together,these partnerships mark a turning point: Pharma R&D is moving from conventional compute clusters to AI-driven HPC ecosystems and hybrid architectures that combine on-premise supercomputing with elastic cloud infrastructure. The implications extend beyond discovery to clinical data management, trial design, and operational analytics. For procurement leaders, this evolution demands new sourcing strategies to balance long-term HPC investments with cloud-based scalability, compliance, and cost optimization.
The Role of Cloud computing in Clinical Development
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While HPC platforms deliver the raw power needed for deep learning, molecular simulation, and generative AI, cloud computing provides the elasticity, scalability, and global reach essential for modern clinical trial operations.This convergence is not optional, and it’s becoming the backbone of data-driven drug development.
Market signals underscore this trend.
The clinical trials cloud computing market is estimated at $2.5 billion in 2025, projected to reach nearly $8 billion by 2033, at a CAGR of ~15%.3
Clinical Trials Cloud Computing Market (in $ Billions)
source: Data Insight Market
The broader pharmaceutical cloud computing market was valued at $4.5 billion in 2023 and is forecast to grow to $15.2 billion by 2032, at a CAGR of ~14.5%.4
Pharmaceutical Trials Cloud Computing Market (in $ Billions)
Source: Data Intelo
Why Cloud Matters for Pharma
## IT Suppliers Pivoting Toward Pharma
technology providers are rapidly reshaping portfolios to meet the growing demand for AI-driven drug discovery and digitally enabled clinical trials. For procurement leaders, understanding these shifts is critical to building resilient, compliant, and cost-effective sourcing strategies. table 2 below summarizes recent strategic moves by key IT suppliers and their relevance for pharma procurement strategies.
Table 2: Strategic Initiatives by IT Suppliers and Their Procurement Implications
Source: NVIDIA Investor News; AWS healthcare Solutions; Thermo Fisher Investor Relations; Clario Newsroom
Conclusion and Future Outlook
The 2025 collaborations between Eli Lilly, Novo Nordisk, and NVIDIA represent more than technology partnerships as they mark the beginning of a new scientific paradigm. By merging AI-driven HPC infrastructures with elastic cloud ecosystems, pharmaceutical R&D is evolving into an interconnected, data-centric model that accelerates discovery and transforms clinical development.
For procurement leaders, the mandate is clear:
- Connect on-premises AI factories with scalable cloud architectures to enable seamless data flow across research and trials. For example, Eli Lilly partnered with NVIDIA to build an AI supercomputer (“AI factory”) integrated with federated learning via TuneLab, while leveraging AWS for elastic trial data processing. This hybrid model accelerated molecule screening and reduced compute bottlenecks.12
- Ensure interoperability, compliance, and auditability in every sourcing decision. Clario’s multiyear agreement with AWS enabled integration of eCOA and remote monitoring tools into HIPAA-compliant cloud environments,ensuring GCP and 21 CFR Part 11 compliance with joint SLAs.13
- Adopt hybrid strategies that balance fixed HPC investments with flexible, cost-optimized cloud resources. For example, Bristol Myers Squibb used AWS for in silico trial simulations, reducing analysis time by 98% and cutting trial duration by nearly a year, while maintaining on-premise HPC for AI model training.14, 15
Cloud computing has moved from a supporting IT function to the core of scientific infrastructure, enabling decentralized trials, real-time analytics, and global“`html
Quantum Computing: A Beginner’s Guide
Quantum computing is a revolutionary field poised to reshape industries from medicine and materials science to finance and artificial intelligence. Unlike classical computers that store facts as bits representing 0 or 1, quantum computers leverage the principles of quantum mechanics to store information as qubits. This allows them to tackle complex problems currently intractable for even the most powerful supercomputers.This guide provides a foundational understanding of quantum computing, its core concepts, and its potential impact.
What is Quantum Computing?
At its core, quantum computing exploits the strange and powerful phenomena of quantum mechanics – the physics governing the behavior of matter at the atomic and subatomic levels. Two key principles underpin this technology:
- Superposition: A qubit can exist in a combination of 0 and 1 together,unlike a classical bit which can only be one or the other. Think of it like a coin spinning in the air – it’s neither heads nor tails until it lands.
- Entanglement: Two or more qubits can become linked together in such a way that they share the same fate, no matter how far apart they are. Measuring the state of one entangled qubit instantly reveals the state of the other.
These principles allow quantum computers to explore many possibilities concurrently, offering exponential speedups for certain types of calculations. However, it’s important to note that quantum computers aren’t meant to replace classical computers entirely. They excel at specific tasks, while classical computers remain more efficient for everyday computing needs.
Qubits vs. Bits
The fundamental difference between qubits and bits is crucial to understanding quantum computing. A bit is a binary digit, representing either 0 or 1. A qubit, however, can represent 0, 1, or a superposition of both. This superposition is mathematically represented as a linear combination of the 0 and 1 states. The more qubits a quantum computer has, the more complex problems it can potentially solve. though, maintaining the delicate quantum states of qubits is a critically important technological challenge.
Applications of Quantum Computing
The potential applications of quantum computing are vast and transformative. Here are some key areas:
- Drug Discovery and materials Science: quantum computers can simulate molecular interactions with unprecedented accuracy, accelerating the discovery of new drugs and materials. IBM Quantum – Drug Discovery
- Financial Modeling: Optimizing investment portfolios, detecting fraud, and assessing risk are all areas where quantum computing can provide a significant advantage.
- Cryptography: Quantum computers pose a threat to current encryption methods, but they also enable the development of quantum-resistant cryptography. NIST Selects First four Quantum-Resistant Cryptographic Algorithms
- Artificial Intelligence: Quantum machine learning algorithms have the potential to significantly improve the performance of AI systems.
- Optimization Problems: Quantum algorithms can efficiently solve complex optimization problems, such as logistics, supply chain management, and scheduling.
Current state of Quantum Computing
Quantum computing is still in its early stages of development. While significant progress has been made,several challenges remain:
- Qubit Stability (Decoherence): Maintaining the fragile quantum states of qubits is extremely challenging. Environmental noise can cause decoherence, leading to errors in calculations.
- Scalability: Building quantum computers with a large number of stable qubits is a major engineering hurdle.
- Error Correction: Developing effective error correction techniques is essential for reliable quantum computation.
- Software Development: Creating quantum algorithms and software tools requires specialized expertise.
Several companies and research institutions are actively working to overcome these challenges. Leading players include IBM, Google, Microsoft, Rigetti Computing, and IonQ. These organizations are pursuing different qubit technologies, including superconducting circuits, trapped ions, and photonic qubits.
Different qubit Technologies
Each qubit technology has its own strengths and weaknesses:
- Superconducting Qubits: Currently the most advanced technology, used by IBM and Google. They are relatively easy to manufacture but require extremely low temperatures.
- Trapped Ions: Offer high fidelity and long coherence
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