ForgeFold

A single-file browser GUI for the whole Boltz-2 workflow: build, run, review.

ForgeFold is a browser-based interface for preparing, running, and reviewing Boltz-2 structure-prediction jobs. It brings Boltz setup, YAML construction, job-script generation, and result inspection into a single lightweight HTML application.

Why it matters: the Boltz-2 workflow normally means hopping between a text editor for YAML, a shell for the run script, and a spreadsheet for the results, with plenty of room for format mistakes along the way. ForgeFold pulls all four stages (set the binary path, build the input YAML from sequences and SMILES, generate the run script, and review confidence and affinity metrics) into one lightweight HTML application that opens in any browser with no build step. It is useful for structure-guided teams who want to lower the friction of repeated co-folding runs and give less command-line-comfortable colleagues a clean, guided path from input preparation to result triage.

Overview

The application is organized around a simple four-step workflow: set the local Boltz-2 binary path, build an input YAML from protein and ligand definitions, configure a run script, and review output metrics from analyzed results.

This design is meant to reduce friction for structure-guided workflows where users repeatedly move between FASTA/SMILES input preparation, command-line execution, and affinity-oriented output review.

Core workflow

1. Setup installation — store the path to the local Boltz-2 executable so it can be reused across sessions.
2. Build YAML input — enter protein FASTA sequences and ligand SMILES, then export an input.yaml file for Boltz-compatible predictions.
3. Configure and write jobs — choose run parameters and generate a run_boltz.zsh shell script for local execution.
4. Analyse results — load processed outputs such as boltz_summary.csv to inspect confidence and affinity-related metrics.

Features

• Browser-based GUI for Boltz-2 workflow management.
• YAML builder for protein and ligand input definition.
• Job-script generation for local prediction runs.
• Results view for summary CSV uploads and prediction review.
• Support for metrics including confidence_score, affinity_pred_value1, and derived pIC50 display.

Input model

ForgeFold is built around Boltz-style YAML inputs that define one or more protein chains and optional ligands. The example interface content shows protein entries with id and sequence, ligand entries with id and smiles, plus optional constraint and property blocks for pocket and affinity settings.

A typical workflow is to paste receptor and partner sequences, add a ligand SMILES string, and export the resulting YAML before launching prediction jobs locally.

Results interpretation

The interface is designed to surface key prediction outputs after external analysis. The bundled UI text highlights confidence_score as an overall model-confidence measure, affinity_pred_value1 as an affinity-related score, affinity_probability_binary1 as a binder-likelihood output, and pIC50 as a derived potency-style estimate.

These values are intended for rapid triage and comparison of modeled complexes rather than as a substitute for experimental validation.

Running ForgeFold

Because ForgeFold is delivered as a standalone HTML application, the simplest way to use it is to open the file in a modern browser and interact with the GUI directly.

Typical usage:

# Example: open the app locally
open ForgeFold.html

Or host it with any lightweight static server:

python -m http.server 8000

Then open http://localhost:8000 in a browser.

Expected companion tools

ForgeFold is designed to sit alongside a local Boltz-2 installation and a downstream analysis script such as analyze_boltz.py, which generates CSV outputs for the Results tab.

To make the full workflow work smoothly, keep the following available:

• A working local boltz executable.
• Input biomolecular sequences and ligand SMILES strings.
• A results-processing script that emits boltz_summary.csv or compatible output.

Suggested repository structure

ForgeFold/
├── README.md
├── ForgeFold.html
├── analyze_boltz.py
├── examples/
│   └── input.yaml
└── outputs/

Roadmap ideas

Potential future improvements suggested by the current UI structure include more robust YAML validation, richer job templating, tighter integration with local output directories, and more detailed downstream result visualizations.

License

Add the project license here.

Source context

This README was drafted from the attached ForgeFold application content and interface text.

👤 Author

Marc C. Deller, D.Phil.
Structural biologist & drug discovery scientist