Discovery
In drug discovery, failure is often accepted as an inevitable part of the process. But what if many of those failures could be avoided? Roughly 70% of Phase II candidates, and about half of Phase III drugs fail because of biological and efficacy gaps, not safety or financing issues. These “protein blind‑spots” waste an estimated US $330 billion annually, while the ~30% of projects that do succeed unlock around US $550 billion in annual drug sales. That’s an enormous opportunity hiding in plain sight.
ASMS was the trigger for me. Watching major pharma demonstrate truly cutting‑edge pipelines made one thing obvious: the future is here - just not evenly distributed yet. Deep, high‑throughput proteomics is now central to finding drugs early that actually work and pass trials - and to capturing knowledge that maps pathways in health and disease. More high‑quality protein evidence closes blind‑spots and, at scale, compounds into a richer understanding that gives every program its best shot and makes the future genuinely transformative.
The hidden cost of protein blind‑spots
Despite major advances in omics technologies, many development teams still guess at which proteins, pathways and interactions matter most during early research. It’s not for lack of data; modern mass‑spectrometry platforms can now generate proteomic data at a scale unimaginable just a few years ago. Since 2022, proteomics throughput has increased ~10×, yet adoption in early‑phase drug programs remains sparse.
Why the disconnect?
To answer that question, we surveyed scientists, biomarker specialists and drug developers across industry and academia. We posed a simple question:
“Despite a 10× jump in proteomics throughput since 2022, it’s still rare in early‑phase drug development. What’s the biggest blocker?”
Responses clustered around four themes:
The takeaway was clear: scientists aren’t ignoring proteomics because they doubt its value; they’re stalled by data complexity, uncertain regulatory pathways and competing priorities. Simply put, the technology has outpaced the ecosystem’s ability to use it.
How real teams are solving the problem
Our customers offer a blueprint for change. At one global pharma R&D group, a recurring proteomics analysis that once consumed 1,000+ hours now runs in ~20 minutes with Mass Dynamics. The platform is embedded on the critical pre‑clinical path for a substantial share of their discovery portfolio - no more slide decks or coordination meetings; chemists get answers the same afternoon. This isn’t just about speed; it’s about removing bottlenecks so scientists can make data‑driven decisions.
Similar stories play out at biotech companies like Nurix Therapeutics, research powerhouses like Johns Hopkins Children’s Center, and contract research labs worldwide. These organisations aren’t just generating proteomic datasets; they’re democratising access to them. By putting user‑friendly analytics and AI‑guided insights in the hands of biologists, they’re de‑risking candidate selection and refocusing budgets on the most promising programs - exactly where our recent poll shows teams need help most (data skills & complexity ranked the top barrier).
Breaking down the barriers
Our poll respondents highlighted several obstacles; here’s how I believe the field should address them:
Data skills & complexity (40% of votes): Cloud‑native proteomics platforms eliminate the need for bespoke bioinformatics pipelines. Intuitive dashboards, automated statistical analysis and AI‑driven annotations allow non‑computational scientists to extract actionable insights without coding. In‑house data science teams can then focus on higher‑value tasks such as custom modelling and validation.
Unclear regulatory endpoints (24%): Regulatory agencies increasingly recognise the value of proteomic biomarkers in decision making. Early engagement with regulators and participation in pre‑competitive consortia can help organisations align on endpoints. Documenting SOPs, validating workflows and ensuring data provenance from day one streamline compliance later.
Budget and priorities (20%): The cost of omics technologies continues to fall, but the biggest savings come from reduced attrition. When teams adopt proteomics earlier, they retire non‑viable candidates sooner and focus budgets on promising programs. Framing proteomics as a risk‑mitigation investment, not a cost centre, can shift budgeting conversations.
Low awareness of scale (16%): Outreach and education remain critical. Sharing success stories and benchmarks (e.g., 1,000 → 20 minutes) shows stakeholders what’s possible today. Hosted workshops, webinars and internal enablement programs help scientists understand that high‑throughput, high‑confidence proteomics is no longer an experimental luxury - it’s a competitive advantage.
The path forward: from SciOps to enterprise value
The proteomics community is on the cusp of a transformation akin to the DevOps revolution in software development. Just as DevOps merged development and operations into a continuous, automated pipeline, SciOps brings data generation, analysis and decision‑making under one coherent framework. In this model, scientists remain the heroes; AI and automation are enablers. Instead of fighting through spreadsheets and siloed analysis, researchers can focus on asking better questions and iterating on hypotheses faster.
Enterprise adoption follows naturally. As more drug discovery organisations adopt unified SciOps platforms, they generate consistent, high‑quality data that feeds back into machine‑learning models. This virtuous cycle not only reduces failure rates but also accelerates regulatory submissions and scales across therapeutic portfolios.
Call to action
The “70% problem” isn’t inevitable. By embedding proteomics earlier in the discovery pipeline, leveraging AI to tame data complexity, and fostering collaboration across R&D, we can shift the odds in our favour.
If your team is grappling with similar challenges, whether it’s data analysis, regulatory uncertainty or simply where to start - let’s connect to tackle these challenges together.
Let’s stop guessing proteins and start unlocking the next generation of therapies.
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This blog post was produced by Assoc. Prof. Andrew Webb using a combination of original notes from discussions and insights. All statistics and quotes referenced are drawn from internal research and published sources. Final compilation was completed with assistance from ChatGPT. Any errors or omissions are unintentional, and the content is provided for informational purposes only. The views, thoughts, and opinions expressed in this text belong solely to the author, and not necessarily to the author's employers, organization, committees or other group or individual
