Is quantum computing a potential solution to pharma’s data bottleneck?

Confronting AI’s structural data ceiling

Recent years have shown us that predictive artificial intelligence (AI) and machine learning (ML) models can speed up specific phases of pharmaceutical research, but we’ve also seen their structural limits. These limitations become apparent in early-stage drug design, where statistical models for molecular simulation are limited without access to large, high-quality historical datasets [1, 2]. When algorithms can’t reliably evaluate new targets, the discovery phase drags, keeping vital therapies out of patients’ hands. Now, as classical computing reaches its limits in processing high-dimensional biological data and simulating complex molecular interactions [3], attention is shifting towards other technological advancements. The UK government recently targeted this bottleneck by revealing a £2 billion package aimed at rolling out quantum computers at scale by the early 2030s [4].

We should view this investment with a measured perspective. The life sciences sector has seen enough tech hype to approach new computational promises with caution. It’s highly likely that quantum computing won’t fix every R&D challenge and that human expertise will yet again prove the most valuable resource. Instead of viewing it as an overarching solution, contract research organizations (CROs) benefit from focusing on its real and practical applications for early-stage development.

At its core, quantum computing powers advanced computational chemistry. It allows researchers to create highly accurate simulations of molecular interactions from scratch to evaluate key molecular and binding properties [5]. These tools reduce the need for lengthy wet-lab experiments. For CROs, the challenge is to determine if this technology can solve the data barriers where current ML fails.

How does quantum computing theoretically differ from machine learning?

In computational chemistry, classical computers force developers into a compromise, as they can’t calculate exact molecular behavior at a quantum level but still deliver results fast enough to be practical for drug design. Because of this speed limit, classical methods rely on approximations that lack the precision needed to accurately model complex biological interactions [5].

ML models hit performance barriers when evaluating complex chemical systems because they rely entirely on historical training data to recognize patterns. For researchers targeting complex, poorly understood diseases or facing insufficient and noisy datasets, this foundational historical data simply does not exist [1, 2]. In these scenarios, the high dependence on pre-existing data becomes a major hurdle [6]. Without sufficient examples to learn from, algorithms produce unreliable results.

In theory, quantum systems bypass this reliance on historical datasets and classical approximations. Quantum computing is designed to directly calculate chemistry from the ground up based on the laws of physics to simulate molecular interactions [5, 7].

Moving quantum computing out of the theoretical

The hardware required to run these calculations is not fully mature, but the physical infrastructure is advancing. Across the industry, a diverse ecosystem of medical research centers and technology developers is actively testing combined systems to see where they provide tangible scientific value. Rather than waiting for perfect hardware, organizations are logging early-stage proofs of concept right now:

• Enzyme-targeted drug design: The National Quantum Computing Centre (NQCC) and Kuano modeled chemical bond formation on a quantum device, specifically addressing the complex transition metal interactions within enzyme inhibitors that classical algorithms struggle to map accurately [8].
• Advancing healthcare infrastructure: The Cleveland Clinic, IBM, and the Hartree Centre are combining AI and quantum computing to build advanced computational frameworks that push past the limitations of standalone algorithms to speed up the discovery of new therapies [3].
• Simulating real prodrugs: Researchers built a hybrid quantum-classical pipeline to simulate covalent bond cleavage for prodrugs like Sotorasib, proving these calculations can be performed within existing workflows today rather than waiting for fully fault-tolerant hardware [9].

To expand this capability further, organizations are actively funding algorithms designed to speed up pharmaceutical research. IonQ and Cambridge University recently formed a strategic research partnership to accelerate quantum discovery, while the Wellcome Leap program provides dedicated funding to develop quantum algorithms that could accelerate fields like genomics and drug discovery [4]. These industry investments parallel recent academic breakthroughs, which project that newer, refined algorithms will eventually allow error-corrected hardware to simulate previously intractable molecular interactions, reducing computing time from an estimated time of over 1000 years down to just a few days [7].

These foundational capabilities are already driving massive market forecasts. Analysts at McKinsey project that by 2035, quantum computing may create between $200 billion and $500 billion in value specifically for the life sciences industry [10]. Long-term financial projections often lean toward optimism, but the underlying necessity for a computational shift is real.

How can early-stage CROs take measured action today?

CROs don’t need to sink massive capital into unproven hardware, but they can take measured steps to prepare their discovery pipelines. Taking action requires focusing on specific, realistic implementations rather than buying into the overarching vendor hype:

• Investigating hybrid models: Organizations can apply early quantum-derived insights into existing classical workflows today. This improves molecular modeling and reaction analysis, advancing high-throughput screening methods even while the industry waits to see if fully fault-tolerant hardware can be realized [9, 11].
• Exploring testbed access: Companies can test these systems without assuming the financial risk of hardware ownership. Dedicated testbeds for deploying and evaluating quantum computers allow developers to test novel algorithms and validate theories [12].
• Identifying complex targets: Service providers should focus their quantum strategy on areas where classical computing explicitly fails. Targeting complex interactions, such as those involving covalent inhibitors, allows developers to test quantum algorithms on problems where classical computational approximations struggle [8].

Translating computational power into patient outcomes

Resolving the data bottleneck is fundamentally about the people waiting for therapies. ML frameworks often stall on complex, low-data targets like rare orphan diseases or Alzheimer’s. This delays the entire development pipeline and leaves patients without viable options. Testing hybrid quantum models today provides a practical route to understanding these difficult biological mechanisms. By prioritizing accurate, physics-based chemistry over tech-industry hype, CROs ensure the development of new therapeutics remains focused on delivering safer and more effective medicines to the people who depend on them.

The ramarketing perspective

The transition from classical ML to quantum infrastructure represents a highly technical shift in commercial positioning. At ramarketing, we help pharma service providers communicate complex scientific capabilities accurately and effectively. We cut through the noise to ensure your expertise in early-stage drug discovery and computational chemistry resonates directly with the biotech and pharma developers who demand evidence over hype.

References
[1]. van Tilborg, D., et al. “Deep learning for low-data drug discovery: Hurdles and opportunities.” Current Opinion in Structural Biology. 86 (2024): 102818.
[2]. Ferreira, Fábio J. N., and Agnaldo S. Carneiro. “AI-Driven Drug Discovery: A Comprehensive Review.” ACS Omega. 10.23 (2025): 23889-23903.
[3]. NQCC. “The convergence of healthcare and pharmaceuticals with quantum computing: A new frontier in medicine.” NQCC. 2025.
[4]. Department for Science, Innovation and Technology. “UK’s ‘Quantum leap’ to help beat disease, deliver high-paid jobs, and strengthen national security, as first country in the world to roll out Quantum computers at scale.” GOV.UK. 2026.
[5]. Santagati, Raffaele, et al. “Drug design on quantum computers.” Nature Physics. 20 (2023): 549-557.
[6]. Jiang, Jian, et al. “A review of machine learning methods for imbalanced data challenges in chemistry.” Chemical Science. 16.18 (2025).
[7]. Blunt, N. S., et al. “Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications.” Journal of Chemical Theory and Computation. 18.12 (2022): 7001-7023.
[8]. NQCC. “Quantum Computing Use Case Compendium.” NQCC. 2025.
[9]. Li, Weitang, et al. “A hybrid quantum computing pipeline for real world drug discovery.” Scientific Reports. 14 (2024): 16942.
[10]. Soller, Henning, et al. “The quantum revolution in pharma: Faster, smarter, and more precise.” McKinsey & Company. 2025.
[11]. NQCC. “Annual Report 2025.” NQCC. 2025.
[12]. Velu, C., and Norman, K. “NQCC Quantum Computing Testbed Pilot Study.” Institute for Manufacturing, University of Cambridge. 2025.