Science-Based AI

Science-Based Artificial Intelligence

AI’s role in scientific research is expanding, and Revvity Signals integrates specialized AI models for applications like molecule synthesis. With a growing library of science-specific AI tools, we provide scientists with advanced modeling capabilities once limited to bioinformaticians.

With a growing library of these science-specific AI integrations, Revvity Signals provides scientists with advanced, yet intuitive, modeling capabilities previously only accessible to bioinformaticians.

Chemical Reactions Recommendations

AI assists scientists by recommending optimal conditions for chemical reactions, including suitable catalysts, solvents, and other key reaction parameters, streamlining the synthesis process.

Key Features

Dynamic Suggestions: Real-time recommendations for catalysts, solvents, and conditions
Interactive Tools: Seamlessly integrated into ChemDraw and Signals Notebook
Confidence Metrics: Includes scores and confidence intervals for reactions
Customizable Models: Tailors algorithms with proprietary data for personalized insights

Why It Matters to You

Accelerate Research: Quickly find optimal reaction conditions
Reduce Errors: Minimize trial-and-error with AI-validated suggestions
Enhance Precision: Make decisions based on high-confidence predictions

Chemicals Reactions Recommendations in Action

The chemical reaction recommendations feature in Signals Notebook leverages AI to provide real-time suggestions for catalysts, solvents, and conditions based on user-drawn chemical reactions.

Integrated directly into the workflow, it offers confidence scores and allows seamless incorporation of recommendations into reaction schemes, enhancing efficiency and accuracy in reaction planning.

Antibody Developability Dashboards

AI evaluates antibody candidates by predicting structures from sequences, estimating accessible surface areas around potential sequence liabilities, and scoring candidates using adjustable developability metrics, enabling scientists to focus on the most promising designs.

Key Features

Sequence Analysis: Detects liabilities and variations in antibody sequences
3D Structural Modeling: Generates precise structural predictions with AI tools like ImmuneBuilder
Liability Scoring: Assesses surface exposure and binding site risks
Interactive Visualizations: Compares multiple antibodies for informed decisions

Why It Matters to You

Accelerate Discovery: Streamline analysis of antibody datasets, saving time and resources
Enhance Precision: Identify high-risk liabilities with surface and structural context
Empower Scientists: Make bioinformatics tools accessible to bench scientists, reducing reliance on specialists

See Antibody Developability Dashboard in Action

The Antibody Developability Dashboard streamlines complex analyses by integrating AI-powered tools to assess antibody sequences for liabilities and developability.

The dashboard provides visualizations of 3D structure predictions, highlights sequence liabilities, and computes metrics like surface accessibility, enabling researchers to evaluate and compare antibody candidates efficiently.