Rosalind AI Co-Scientist

Your intelligent research assistant that automates workflows and surfaces insights

Rosalind is LiteFold’s AI co-scientist that brings automation, intelligence, and efficiency to your drug discovery research. Named after Rosalind Franklin, the pioneering crystallographer, Rosalind helps you navigate complex research workflows, find relevant information, and make data-driven decisions.

What is Rosalind?

Rosalind is an AI-powered research assistant that:

• Searches across protein, literature, and experimental databases
• Automates experiment setup and configuration
• Analyzes results and provides actionable insights
• Suggests next steps and alternative approaches
• Explains complex results in natural language

Think of Rosalind as a knowledgeable colleague who’s always available to help with your research.

How Rosalind Works

1. You Ask a Question

   Start by asking Rosalind a question or describing what you want to accomplish.

   Examples:

   • “What are known inhibitors of EGFR?”
   • “Set up a docking experiment for this protein”
   • “Find similar structures in the PDB”
2. Rosalind Searches and Analyzes

   Rosalind searches across multiple databases and knowledge sources:

   • Protein Data Bank (PDB)
   • UniProt
   • ChEMBL
   • Scientific literature
   • LiteFold’s internal datasets
3. You Choose a Direction

   Rosalind presents findings and suggestions. You select the approach that fits your research goals.

4. Rosalind Sets Up Experiments

   Based on your choice, Rosalind automatically configures experiments with optimal parameters.

Key Capabilities

Database Search and Integration

Rosalind has direct access to major scientific databases and can retrieve information in seconds.

Protein Databases

• PDB: Search by protein name, organism, or function
• UniProt: Retrieve sequences, annotations, and functional data
• AlphaFold DB: Access predicted structures
• InterPro: Find protein domains and families

Compound Databases

• ChEMBL: Known bioactive molecules and targets
• PubChem: Chemical structures and properties
• DrugBank: Approved drugs and drug candidates
• ZINC: Virtual screening libraries

Literature & Data

• PubMed: Research articles and reviews
• Patent databases: IP searches
• Clinical trials: Drug development data
• BindingDB: Binding affinity measurements

Intelligent Experiment Setup

Rosalind can configure experiments automatically based on your target and goals.

Structure Prediction

You: "Predict the structure of human CDK2"
Rosalind:
- Retrieves CDK2 sequence from UniProt
- Selects optimal prediction model (AlphaFold2)
- Configures parameters for kinase domain
- Initiates prediction job

Virtual Screening

You: "Screen FDA-approved drugs against this protein"
Rosalind:
- Identifies binding pocket automatically
- Loads FDA-approved drug library
- Sets up docking grid and parameters
- Launches high-throughput screening

Comparative Analysis

You: "Compare this structure to similar proteins"
Rosalind:
- Searches PDB for structural homologs
- Performs structural alignments
- Highlights key differences
- Suggests mutations for validation

Result Analysis and Insights

Rosalind doesn’t just run experiments—it helps you understand the results.

Pattern Recognition

Identifies trends, outliers, and significant findings in your data automatically.

Natural Language Explanations

Explains complex results in plain language, making findings accessible.

Literature Context

Connects your results to published research and known biology.

Next Step Recommendations

Suggests follow-up experiments based on your current results.

Example Workflows

Target Identification

1. Ask Rosalind

   “What proteins are associated with Alzheimer’s disease and are druggable?”

2. Rosalind Searches

   • Queries disease-gene associations
   • Identifies proteins with known binding pockets
   • Ranks by druggability scores
   • Provides summary of each target
3. Review and Select

   Review Rosalind’s findings and select a target to pursue (e.g., BACE1).

4. Automatic Setup

   Rosalind retrieves structure, known inhibitors, and sets up your first docking experiment.

Hit-to-Lead Optimization

1. Share Initial Hit

   “I have this compound that binds weakly. How can I improve its affinity?”

2. Rosalind Analyzes

   • Analyzes binding mode and interactions
   • Identifies areas for improvement
   • Suggests modifications (R-groups, scaffolds)
   • Predicts ADMET properties
3. Generate Analogs

   “Generate 20 analogs with better predicted affinity”

4. Rosalind Designs

   • Uses generative models to create analogs
   • Filters by drug-likeness
   • Ranks by predicted affinity
   • Provides synthetic accessibility scores

Competitive Intelligence

1. Ask About Competition

   “What compounds are in clinical trials for KRAS G12C?”

2. Rosalind Researches

   • Searches clinical trial databases
   • Retrieves patent information
   • Identifies chemical structures (when available)
   • Summarizes mechanism of action
3. Comparative Docking

   “Dock these competitive compounds against my structure”

4. Rosalind Analyzes

   • Performs docking for all compounds
   • Compares binding modes
   • Highlights unique features of each
   • Suggests differentiation strategies

Natural Language Commands

Rosalind understands natural language. Here are examples of what you can ask:

Search and Discovery

• “Find kinase inhibitors with IC50 < 10 nM”
• “Show me GPCRs with solved crystal structures”
• “What mutations confer drug resistance in EGFR?”

Experiment Setup

• “Set up a virtual screening with the Enamine library”
• “Run MD simulation of this complex for 100 ns”
• “Generate 50 molecules to fit this binding pocket”

Analysis

• “Why did compound A bind better than compound B?”
• “Which residues are critical for binding?”
• “What’s the predicted bioavailability of this molecule?”

Optimization

• “Improve the solubility of this compound”
• “Reduce the molecular weight below 500 Da”
• “Design a covalent inhibitor for Cys797”

Advanced Features

Context Awareness

Rosalind remembers your conversation and builds context over time.

You: "Predict the structure of CDK2"
Rosalind: [Runs structure prediction]

You: "Now dock ATP to it"
Rosalind: [Understands "it" refers to CDK2, docks ATP]

You: "What about ADP?"
Rosalind: [Docks ADP to the same protein]

Multi-Step Workflows

Rosalind can execute complex, multi-step workflows automatically.

You: "Run a complete lead optimization on this hit compound"

Rosalind:
1. Analyzes binding mode and interactions
2. Generates 100 analogs with modifications
3. Filters by drug-likeness (Lipinski's Rule of 5)
4. Docks all analogs
5. Predicts ADMET properties for top 20
6. Runs short MD simulations on top 5
7. Presents ranked list with detailed report

Custom Workflows

You can teach Rosalind new workflows specific to your research.

You: "Create a workflow called 'kinase screen' that includes:
     1. Structure prediction
     2. Binding site identification
     3. Docking with kinase inhibitor library
     4. MD validation of top 3 hits
     5. Generate report"

Rosalind: "Workflow 'kinase screen' saved. You can now run it
           by saying 'Run kinase screen on PROTEIN_NAME'"

Collaboration Features

Team Knowledge Sharing

Rosalind learns from your team’s collective research:

• Shares insights across team members
• Remembers successful approaches
• Suggests strategies that worked for similar projects
• Maintains institutional knowledge

Annotations and Notes

Work with Rosalind collaboratively:

You: "Add note to this docking result: promising scaffold for SAR study"
Rosalind: "Note added. Would you like me to flag similar
           scaffolds in future experiments?"

Privacy and Data Security

• Your data stays private: Rosalind queries public databases, but your proprietary data never leaves LiteFold
• Secure processing: All analyses run in isolated, encrypted environments
• Audit trail: Complete logs of all Rosalind actions for compliance and reproducibility

Tips for Working with Rosalind

Be specific but natural: Rosalind understands both technical terms and plain language. “Dock compounds” and “Perform molecular docking simulation” both work.

Ask follow-up questions: If a result isn’t clear, ask Rosalind to explain. “Why did you choose those parameters?” or “What does this binding mode suggest?”

Provide context: The more context you give, the better Rosalind can help. “I’m designing a blood-brain barrier penetrant” helps Rosalind filter and suggest appropriately.

Verify critical decisions: While Rosalind is highly capable, always review important experimental decisions. Rosalind is a tool to augment, not replace, your expertise.

Limitations

Rosalind is powerful but has some current limitations:

• Cannot perform wet-lab experiments (yet!)
• Limited to publicly available data for searches
• May occasionally misinterpret ambiguous requests
• Cannot access real-time experimental data from other labs

We’re continuously improving Rosalind’s capabilities based on user feedback.

Getting Started with Rosalind

Ready to work with Rosalind? Here’s how to start:

1. Click “Ask Rosalind” in any LiteFold project
2. Describe what you want to do in natural language
3. Review Rosalind’s suggestions and choose your path
4. Let Rosalind automate experiment setup and execution

Try Rosalind Now

Log in to LiteFold and start chatting with Rosalind

Examples from Real Research

See how researchers are using Rosalind:

EGFR Inhibitor Design

How Rosalind helped design novel EGFR inhibitors for resistant mutations

Virtual Screening

Using Rosalind for high-throughput virtual screening campaigns

Feedback and Feature Requests

Have ideas for how Rosalind could be more helpful? We’d love to hear from you!

Email us at [e-mail] with feedback or feature requests.