Revvity Signals Briefing Note
Axendia was recently briefed by Chris Stumpf, Director of Drug Discovery Informatics Solutions, and Diana Tran, Sr. Product Marketing Specialist, of Revvity Signals Software, a division of Revvity, Inc. Signals One, an end-to-end SaaS solution for Drug Discovery and complex biological workflows, aims to address the ongoing problem of siloed data by making data more FAIR (Findable, Accessible, Interoperable, Reproducible) and by enabling cross-disciplinary collaboration amongst scientific teams and between sponsors and external contract partners.
Building Upon Existing Legacy
The journey for Revvity Signals (formerly PerkinElmer Informatics) began with the founding of CambridgeSoft and the launch of ChemDraw in 1985 – the industry standard in chemical structure drawing. With Revvity Signals’ acquisition of CambridgeSoft in 2011, Revvity Signals gained the foundational expertise to build what would become Signals Software, “allowing the company to sharpen its focus on accelerating scientific discovery across the industries it serves,” stated Stumpf.
The transition from desktop software to cloud-native SaaS solutions reflects a broader industry shift, a notable evolution that applies to Signals Notebook. Tran explained that “Signals Notebook is now a comprehensive, cloud-based solution built on SaaS best practices. We have ten release cycles per year, automatic upgrades with no downtime, included with the price of the subscription. Revvity Signals prides itself on rapid incorporation of customer feedback while maintaining consistent software versions across all users.”
In addition, she stated, “The platform that enables Signals Notebook maintains full security including AI-based endpoint protection, automated backups, disaster recovery, and required certifications like SOC 2 [Systems and Organization Controls 2] compliance. Because of these attributes, our customers really enjoy using the software, evidenced by a 97% renewal rate. The platform serves over one million end users worldwide across more than 4,000 customers, including all the top 20 biopharma companies.”
Design, Make, Test, – AND Design
With regards to the traditional DMTA (Design, Make, Test, Analyze) cycle, the iterative process for designing drugs and formulating new hypotheses, Revvity Signals emphasizes “Decide” instead of “Analyze” in that last step. This adjustment reinforces the importance of focusing on what to do next and driving subsequent actions – in real time – rather than taking the proverbial pause. Stumpf remarked that, “This reflects the solution’s built-in analytics capabilities that help users move beyond mere data presentation to actionable decision-making in Signals One.” The solution’s cross-disciplinary approach integrates chemistry, biology, materials R&D, assay data, analytics, and lab notebooks into a single ecosystem – addressing a fundamental challenge Axendia has presented in its ongoing analysis of the digital lab and product lifecycle management. The emphasis placed on “decide” reflects an important truth in drug discovery – that analysis is only valuable when coupled to action.
Signals One is comprised of three integrated components:
This architecture facilitates the design-make-test-decide paradigm across different drug modalities, from traditional small molecules to complex antibody-drug conjugates.
Leaning into the Complexity
With such a diversity of therapeutic modalities in the biopharma pipeline, and with new entities always on the horizon, drug programs are no longer linear or discipline-bound. These inherent interdependencies have had a significant influence on the Revvity Signals team’s adopting a more agile approach to software updates, resulting in more frequent release cycles each year. Stumpf stated that “legacy or fragmented tools increasingly become bottlenecks as research complexity grows. Timing is critical – prolonged cycles increase cost, risk, and time to bring therapies to market.”
The volume of data, internal and external collaborations, regulatory scrutiny, and need for reproducibility all demand robust data infrastructure. As Axendia has discussed previously, technologies such as AI/ML and advanced analytics require more structured, integrated, and scalable data environments than traditional systems can provide. The strategic management of scientific data has become a competitive differentiator.
Standardization of data formats continues to be a challenge for many companies looking to take advantage of these technologies because structured data, aligned with FAIR data principles, is crucial for effective supervised learning. Revvity Signals’ approach to AI readiness builds on a fortunate foundation: their long-standing integration with Spotfire, a scientific analytics and visualization solution, requires labeled data sets, which deliver data schemas that align well with machine learning requirements. The solution now incorporates AI capabilities including semantic search, image to text search, and will provide Models as a Service (MaaS) within the research context to accelerate drug discovery timelines, while maintaining an open framework that allows easy integration of third-party AI/ML tools.
In Brief
The drug discovery landscape demands integrated digital platforms that can handle complexity, while enabling collaboration, and maintaining data integrity. Revvity Signals is addressing these needs through a cloud-native SaaS architecture that bridges traditional silos between disciplines and organizations. As scientific complexity continues to increase and AI-driven research becomes standard practice, platforms that provide structured, accessible, and interoperable data will become increasingly critical to successful drug development programs.
We will continue to provide updates on Revvity Signals as they become available.
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.