From Data Wrangling to Discovery: Fixing the Integration Crisis in Drug Discovery

Drug discovery is drowning in data, but not in insights. At the recent Proventa Medicinal Chemistry Roundtable, industry leaders agreed that the real bottleneck isn’t a lack of technology: it’s fragmented, poorly integrated data. Without clean, contextualized, and interoperable datasets, even the most advanced tools, including AI, cannot deliver their full potential.

So how do we move from chaos to clarity? Here’s what experts had to say.

The Productivity Paradox

Despite the proliferation of sophisticated platforms, researchers are still spending disproportionate amounts of time wrangling data instead of generating insights. The roundtable captured this frustration perfectly when one participant remarked: “We don’t know what we don’t know.”

This statement underscores a critical issue: data exists, but it’s often locked away in silos, mislabeled, or stored in incompatible formats. Scientists waste hours searching for information that should be readily available, and even when they find it, the lack of context or standardization makes integration a nightmare.

The consequences are far-reaching. Decisions made on incomplete or poorly annotated datasets can derail entire projects, leading to costly delays and missed opportunities. In fast-moving organizations, this risk is amplified : teams may unknowingly duplicate work or overlook key findings simply because the data wasn’t accessible or interoperable.

The paradox is clear: while technology promises speed and efficiency, the reality is that poor data integration slows everything down. Instead of accelerating discovery, researchers are bogged down in manual processes, copying, cleaning, and reconciling data, just to make it usable. This isn’t just a productivity problem; it’s a strategic one. Without integrated, high-quality data, organizations cannot fully leverage advanced analytics or AI-driven insights.

Signals DLX™ powered by Scitara tackles this challenge head-on by connecting instruments, LIMS, ELNs, and other critical systems through the industry’s first integration Platform-as-a-Service (iPaaS) for science. By breaking down silos and automating data flows, DLX ensures researchers spend less time on manual reconciliation and more time on discovery.

FAIR in Theory, Messy in Practice

The FAIR data principles (Findable, Accessible, Interoperable, and Reusable,) are widely regarded as guidelines to follow for proper data management. However, implementing them without disrupting scientific workflows remains a major challenge.

Roundtable participants described the tension between rigorous data governance and the need for agility, especially in smaller companies. “Some centralized systems slowed productivity, while decentralized systems undermined reproducibility”, one roundtable participant remarked.

This balancing act often leaves organizations stuck in limbo: too much control stifles innovation, while too little creates chaos. The result? The FAIR data principles remain aspirational rather than operational. For data FAIRification to work, it must be embedded into everyday processes, and not treated as an afterthought or compliance checkbox.

Signals Notebook operationalizes the FAIR data principles by providing a modern ELN that integrates ChemDraw™ and Spotfire®. It enables seamless data capture and collaboration while maintaining compliance and reproducibility. Combined with Signals DLX, it ensures FAIR isn’t just theoretical, it’s practical and embedded in daily workflows.

Ontologies and Future-Proofing

One of the most compelling insights from the discussion was the need to embed ontologies and controlled vocabularies directly into experimental workflows. Rather than asking scientists to retroactively clean and annotate data, the goal should be to capture structured, contextualized information at the point of generation.  As one participant summed it up: “Bake it in by design, not by request.”

This approach future-proofs data for advanced analytics and AI applications. When data is captured with consistent terminology and rich metadata from the start, it becomes interoperable across systems and reusable for years to come. It also reduces the burden on researchers, freeing them time to focus on science rather than data cleanup.

Signals One™ is designed with this principle in mind. As a unified, cloud-native SaaS platform supporting the entire Design-Make-Test-Decide lifecycle, Signals One ensures data is captured consistently and enriched with metadata from the start, making it interoperable and future, ready for advanced analytics and AI.

AI Is Not a Silver Bullet

AI is often touted as the solution to drug discovery’s biggest challenges. However, without well-curated and properly documented data, it can mislead more than help. Historical datasets, often inconsistent or poorly annotated, were cited as major barriers to effective reuse.

The takeaway?  Data integrity first, algorithms second.

AI models like LLMs and retrieval-augmented generation (RAG) hold enormous promise, but they depend on structured, contextualized data to deliver accurate predictions. Organizations that invest in robust data integration today will be the ones that reap the benefits of AI tomorrow.

With AI-Enhanced Workflows, Revvity Signals leverages clean, structured data to power semantic search, summarization, and predictive modeling. By combining robust data integration with embedded AI tools, organizations can trust that insights are accurate and actionable.

The Case for Radical Collaboration

True breakthroughs will require rethinking data sharing across CROs, academia, and pharma. While IP protection remains a barrier, participants advocated for model sharing rather than raw data exposure and publishing negative results to improve predictive models.

Collaboration isn’t just a nice-to-have, it’s a necessity. AI models learn as much from what doesn’t work as from what does. Sharing failed experiments and negative results can dramatically improve the accuracy and reliability of predictive analytics, accelerating innovation across the industry.

Signals solutions enable secure, role-based collaboration across internal teams and external partners. By providing controlled access and audit trails, Revvity Signals fosters collaboration without compromising IP, creating an ecosystem where data and insights flow freely but securely.

Build with the End in Mind

Across roles and industries, one theme stood out: data strategies must be purpose-driven. Whether planning acquisitions, validating IP, or enabling AI-driven insights, capturing clean, contextual, and interoperable data now will pay exponential dividends later.

As one speaker aptly put it: “There’s a price to pay for chaos, and a price to pay for perfection. Each organization has to find its sweet spot.”

The message is clear: data integration isn’t just about technology : it’s about mindset, alignment, and designing for the future of science.

By unifying workflows through Signals One, ensuring data FAIRification with Signals Notebook, and breaking down silos with Signals DLX, Revvity Signals empowers R&D organizations to design data strategies that scale, future-proofing science and enabling AI.

Bottom Line

Solving the data integration crisis isn’t just about technology it’s about mindset, alignment, and designing for the future of science. With Revvity Signals, you can operationalize the FAIR principles, unify your data ecosystem, and accelerate discovery.

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