Artificial intelligence is increasingly embedded in molecular and materials research, reshaping how laboratories approach early discovery. A new AI-augmented molecular design platform integrates computational modeling, predictive analytics, and experimental validation to move beyond traditional trial-and-error experimentation. Revvity’s introduction of Signals Xynthetica reflects this broader shift toward operationalizing AI within routine laboratory workflows.
Signals Xynthetica is introduced as a models-as-a-service offering within the Revvity Signals platform. The AI-augmented molecular design platform embeds predictive models directly into scientific workflows, allowing research teams to iteratively design, test, and refine candidate molecules and materials while maintaining data governance and scientific rigor. By linking in-silico predictions with experimental outcomes, the platform is intended to support continuous learning across discovery programs.
What the AI-augmented molecular design platform is designed to support
The platform supports a range of in-silico design approaches commonly used in modern discovery environments, including de novo molecular generation, property prediction, and multi-objective optimization. Rather than requiring laboratories to build and maintain internal AI infrastructure, predictive models are delivered centrally and applied consistently across projects.
Models operate within a governed environment, enabling organizations to refresh and evaluate them transparently as new experimental data becomes available. For laboratory teams, this structure supports side-by-side comparison of computational predictions and experimental results, reinforcing data-driven decision-making throughout early discovery.
Connecting predictive molecular discovery to experimental data
A defining aspect of the platform is its emphasis on connecting predictive molecular discovery with wet-lab validation. Predictive models generate candidate molecules or materials, which are then synthesized or tested experimentally. Resulting data feeds back into the system, allowing models to be refined as additional evidence accumulates.
“AI has enormous potential to transform how molecules and materials are designed, but that potential is only realized when models are connected to real scientific workflows and data,” said Kevin Willoe, president of Revvity Signals Software. “The Signals Xynthetica platform is about operationalizing predictive science that brings in-silico design and experimental validation together in a continuous loop, accelerating discovery across industries.”
Operational considerations for laboratory managers
For lab managers, AI-augmented molecular design platforms raise practical considerations related to staffing, infrastructure, and workflow consistency. Delivering predictive capabilities as a service reduces the need for dedicated internal AI development teams while broadening access to advanced modeling tools across research groups.
The platform’s focus on governance and data stewardship also aligns with increasing expectations around reproducibility, traceability, and audit readiness. These considerations are particularly relevant for laboratories operating in regulated research environments or managing cross-functional discovery programs.
Supporting in-silico and experimental workflows at scale
By embedding predictive models directly into existing discovery workflows, the platform aligns computational design with laboratory execution. This integration helps ensure that predictive outputs remain grounded in experimental reality while supporting scalable use across multiple projects.
As AI adoption expands across life sciences and materials research, platforms that unify in-silico and experimental workflows are becoming more routine in laboratory operations rather than specialized capabilities.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
