In pharmaceutical R&D, most leaders believe the constraints are scientific: new biology, rare targets, combinatorial complexity. But across the industry, promising drug discovery pipelines are being delayed – not by a lack of scientific progress, but by infrastructure that can’t keep pace. The next transformative molecule won’t be held back by biology – it will be held back by architecture, orchestration, and inefficient compute.
The time to shift mentality from “buy more hardware” to “optimize infrastructure” is now – and the teams who adopt that shift will pull ahead in discovery timelines, cost control, and scientific throughput.
The invisible bottleneck: compute inefficiency
Let’s start with a blunt truth: GPUs are mostly idle. Across many AI/ML and scientific workloads, utilization typically sits in the 35–65% range – meaning you’re paying for compute that sits dormant. Meanwhile, deployment windows stretch from hours to days, teams wait for environments to spin up, and researchers lose precious cycles squabbling with infrastructure teams rather than iterating science.
That’s not a theoretical risk – it’s a real drag on productivity. A 2024 survey conducted by ClearML and the AI Infrastructure Alliance revealed that 74% of organizations are dissatisfied with their scheduling tools, and only 19% actually use infrastructure-aware scheduling to optimize GPU allocation. In essence: most teams can’t fully access the compute they already have.
That inefficiency compounds when you consider drug pipelines often require hundreds or thousands of parallel experiments. Delays multiply across these workflows. The infrastructure overhead becomes a hidden, but crippling, cost.
When infrastructure works with science
To see what’s possible when infrastructure is no longer a barrier, look to the Cornell-led “Pandemic Drugs at Pandemic Speed” HPC research. That work screened over 12,000 molecules in 48 hours using hybrid AI and physics-based simulations across four geographically distributed supercomputers. Binding simulations were executed in parallel across 1,000+ compute nodes.
The success hinged on modular infrastructure and orchestration tools (like RADICAL-Cybertools) that enabled elastic scaling across regions, efficient job scheduling, and minimal overhead of configuration. The pipeline was built so workflows could scale, shift, and adapt – not so the infrastructure itself became a bottleneck.
The R&D tech stack is crumbling under the old model
McKinsey’s 2025 report, Boosting biopharma R&D performance with a next-generation technology stack, confirms the reality many already see: the biggest gains in R&D won’t come from better models or bigger clouds, but from modernising the foundation.
That report argues that many pharma R&D organizations are constrained by legacy systems, siloed data, and brittle point-to-point integrations. These make it hard to deploy workflows, reuse data across discovery and clinical phases, or integrate advanced AI efficiently.
Their recommendation: move to a modern stack with clearly defined layers – infrastructure, data, application, analytics. Such integration enables:
- Faster iteration between experiments and insights
- Cost savings through reduced tech debt and improved resource efficiency
- Seamless handoffs from discovery into clinical phases
You can’t bolt AI and simulation workflows onto rigid infrastructure and expect acceleration. The foundations must change.
Why more hardware isn’t the answer
When researchers face slow pipelines, the default response is often to buy more GPUs. But this rarely solves the underlying issues. It amplifies them.
Additional hardware doesn’t address scheduling gaps, orchestration blind spots, or job fragmentation. Without an intelligent system to orchestrate how resources are allocated, new hardware often ends up underutilized – stranded in silos or misaligned with workload requirements. What’s needed is not more compute, but better coordination of the resources already in place.
In short: buying more compute magnifies the cost of inefficiency. The real gains come from making what you already have work smarter.
The shift: orchestrate smarter, not scale bigger
If the problem is orchestration, not availability, then the solution is infrastructure software that treats all compute – cloud, on-prem, bare metal – as a single pool. That’s where the idea of a Unified Compute Plane, like Orion, becomes critical. By abstracting infrastructure into a single control layer, it becomes possible to orchestrate workloads with far greater flexibility and efficiency. Container-native by design, this approach supports rapid deployment, dynamic scheduling, and intelligent GPU allocation, including slicing and multi-instance capabilities. In practice, this model has helped organizations cut deployment time from 72 hours to 15 minutes, drive GPU utilization to 92%, and reduce compute costs by more than half – all while giving research teams the ability to iterate in real time.
This isn’t about ripping and replacing. A Unified Compute Plane integrates with your existing infrastructure, bridging silos and eliminating idle capacity without locking you into proprietary ecosystems. It’s a software shift, not a forklift upgrade.
Beyond infrastructure: enabling a new R&D operating model
McKinsey outlines a future in which R&D is modular, agile, and data-driven. But that future relies on infrastructure that supports those values. Without unified orchestration, there is no agility. Without reliable, high-utilization compute, there is no speed.
This is not just an IT transformation – it’s a scientific transformation. The ability to rapidly simulate, iterate, and validate hypotheses is what turns good ideas into viable drug candidates. Infrastructure that supports this cadence becomes a force multiplier for scientific output.
Alex Hatfield
For CIOs, this is about turning a sunk cost into a strategic advantage. For Heads of Research, it’s about unblocking scientific throughput. For infrastructure leaders, it’s about finally eliminating the artificial boundaries between environments.
A Unified Compute Plane offers a new path – one where infrastructure empowers science instead of delaying it. The next breakthrough drug won’t be discovered in a vacuum. It will emerge from an R&D engine that can test faster, adapt quicker, and compute smarter. The organizations who recognize this – and act – will lead the next era of pharmaceutical innovation.
Alex Hatfield is CEO, Juno Innovations.
Filed Under: Data science
