Overview
Boltz2 Cofolding is Rev-Bind’s AI-powered structure prediction engine. Unlike classical docking, which requires a pre-determined 3D protein structure and a defined binding pocket, Boltz2 takes just two inputs — a protein sequence and a SMILES string — and generates a co-folded 3D structure showing the ligand placed within its most biologically relevant binding pocket. This makes Boltz2 an especially powerful early-stage tool: you can generate binding pose predictions and activity metrics without needing a crystal structure or experimental pocket data.How Boltz2 Cofolding Works
Boltz2 runs an AI engine that concatenates the protein sequence and small molecule structure, then simultaneously folds and docks them together. The resulting co-folded structure is a prediction of the binding pose — specifically, how the ligand sits within the pocket that drives the target’s biological activity. For example, with a kinase target the engine will co-fold the ligand into the ATP-competitive hinge region pocket — placing the compound where it would need to bind to produce a therapeutic effect — without requiring you to first define that pocket manually.Comparison to Classical Docking
| Classical Docking (RevScreen) | Boltz2 Cofolding | |
|---|---|---|
| Input | 3D protein structure + ligand 3D conformation | Protein sequence + SMILES |
| Pocket definition | Required (manual or RevPocket) | Automatic |
| Conformational flexibility | Limited | Full co-fold |
| Best for | Large-scale library screening | Early-stage pose prediction, novel targets |
| Output | Docking scores, poses | Co-folded structure, confidence, affinity, pIC50 |
Output Metrics
Each co-folded result returns three primary metrics:| Metric | Description |
|---|---|
| Confidence Score | The model’s confidence in the predicted binding pose — reflects structural reliability |
| Affinity Probability | A heuristic estimate of binding affinity — useful for relative ranking across a compound set |
| Predicted pIC50 | The primary activity outcome — a predicted potency metric derived from the co-folded pose |
Click the i icon next to any metric in the results panel for a plain-language explanation of what it means and how to interpret it.
Running a Boltz2 Pipeline
Open Data Engineering
Click into Data Engineering. You’ll see two input fields: one for the protein sequence and one for the SMILES string.If you don’t have a campaign running yet, use the demo files to follow along — they contain all the required inputs pre-formatted.
Input Your Protein Sequence
The protein sequence should be provided as a CSV file with a single chain column (e.g.,
AXLChainA) containing only the raw amino acid sequence string.You have two options:- Drag-drop your CSV file directly into the data engineering pane
- Click Add Manually and paste the sequence string directly into the input field
Input Your SMILES String
The small molecule is defined by its SMILES string. You can provide this as:
- A CSV file containing one or more SMILES strings for batch cofolding
- Manual entry — paste a single SMILES string directly into the input field
Name Your Pipeline
Enter a descriptive name for the pipeline (e.g.,
AXL-boltz2-type2-inhibitors). This name will identify the run in your pipeline history.Select the Pipeline Type
Set the Pipeline Type to Affinity for standard protein-ligand cofolding. Additional co-folding types are available for more complex analyses:
- Affinity — Protein + ligand cofolding for binding pose and activity prediction (standard)
- Multimer — Co-fold multiple protein chains together
- Additional types for varied protein-small molecule configurations
Make sure to select the pipeline type that matches your experimental setup. Using the wrong type will produce mismatched output.
Reading and Navigating Your Results
Once the pipeline completes, navigate to your results from the pipeline hub.Viewing Co-folded Structures
The primary output is an interactive 3D viewer showing the co-folded protein-ligand complex. The ligand will be placed within the highest-fidelity pocket — the site most likely to drive biological activity for that target class. For kinase targets, for example, you’ll see the compound positioned within the hinge region — competing with ATP in the canonical ATP-binding site, which is exactly the mechanism a type II competitive inhibitor would engage.Navigating Large Result Sets
When running campaigns with many compounds, use the results panel to browse your full ligand list. You can jump directly to any specific compound by number (e.g., Ligand 450) rather than scrolling through the entire set.Exporting Data
For downstream analysis of large batches, click Export CSV to download your full results table. This includes all confidence scores, affinity probabilities, and predicted pIC50 values for every compound in the run — ready for statistical analysis, filtering, or integration into your own workflows.Calibrating Boltz2 Outputs
Boltz2’s predicted metrics are powerful heuristics for relative ranking within a compound set. They are not absolute experimental measurements. Best practice is to:- Run Boltz2 on a set of compounds that includes known actives and known inactives from your target
- Validate that the predicted pIC50 and confidence scores rank-order consistently with your experimental data
- Use this calibration to interpret predictions for novel compounds with greater confidence
Triaging to Protein-Ligand MD
Once you have identified your highest-confidence co-folded poses, you can triage them directly into Protein-Ligand MD for more robust validation. From the results panel, select your compound(s) of interest and click Triage to Protein-Ligand MD. This will:- Export the co-folded pose as the starting structure for MD
- Queue an MD simulation to assess binding stability and residence time
- Enable MMPBSA and MMGBSA rescoring for more rigorous free energy estimates
Next Steps
After Boltz2 cofolding, your compound list is ready for:- Protein-Ligand MD — RevMD-Bind — Triage top poses directly to MD for stability validation and MMPBSA/MMGBSA rescoring
- RevFEP — Rigorous alchemical free energy calculations for final-stage candidate ranking
- RevScreen - Static & Flexible Docking — Run classical docking screens on the same compound set for orthogonal validation
- RevScreen - Ensemble Docking — Use MD-sampled protein conformations for maximum-accuracy virtual screening on shortlisted leads