Drug Discovery
From idea to IND, for every modality. Bring your disciplines together, from small molecules to large modalities. Accelerate your drug discovery with Signals One™ by eliminating your data silos and empowering your users.
Leading Drug Discovery Organizations Choose Revvity Signals
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Drug Discovery FAQs
Revvity Signals supports chemical and biological research in the same connected working environment. Chemists and biologists use tools designed for their specific workflows while keeping structures, sequences, assays, and results linked. This allows teams to collaborate across disciplines without forcing one group to change how they work.
Revvity Signals is strongest in small molecules, antibodies, antibody–drug conjugates (ADCs), and RNA therapeutics, where workflows, data models, and analytics are deeply supported. The environment can also support additional modalities such as cell-based therapies, T-cell programs, vaccines, and emerging approaches by enabling structured experimental capture, data connectivity, and analysis. While support depth varies by modality, teams benefit from a consistent way to manage, analyze, and interpret discovery data across programs.
Teams usually start with the tools most relevant to their science. Small molecule programs often begin with ChemDraw® and Signals Notebook for design and experimental capture. Antibody, ADC, and RNA teams typically start with Signals Notebook or Signals BioDesign for biological design, assay tracking, and experimental documentation. Teams can expand as programs mature without reworking earlier data.
ADCs require tight coordination between chemistry and biology. Revvity Signals connects payload and linker chemistry with antibody biology, assays, and experimental results in one working environment. This improves traceability, reduces handoffs, and helps teams align faster across complex, hybrid discovery programs.
Signals One focuses on secondary data analysis, data analytics, and data intelligence. It brings together results from across discovery to support comparisons, surface insights, and enable confident decision-making. This allows teams to move from data generation to decisions without manual aggregation or disconnected analysis.
By keeping chemistry and biology data connected and layering analytics and intelligence on top, teams can evaluate results in context, identify trends earlier, and make informed decisions faster. This reduces guesswork and supports clearer progression from early discovery through lead selection.
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