Boltz-2 Integration: Accelerating Structural Prediction and Binding Affinity Analysis on Vecura

What is Boltz-2?

Boltz-2 is an advanced, open-source biomolecular foundation model designed to jointly predict both the 3D structures and binding affinities of complex molecular systems. By unifying structural prediction and biophysical scoring into a single inference pass, it offers a powerful alternative to traditional methods. It is particularly valuable for drug discovery researchers who need to rapidly screen and rank thousands of protein–ligand candidates with efficiency that approaches physics-based Free Energy Perturbation (FEP) methods at a fraction of the computational cost.

What can users do with Boltz-2 on Vecura?

With Boltz-2 on Vecura, users can:

• Predict 3D structures for complex assemblies containing proteins, DNA, RNA, and small-molecule ligands in a single workflow.
• Perform simultaneous co-folding and docking, eliminating the need for separate, manual docking steps.
• Score binding affinities to accurately rank ligands, using calibrated binding probabilities to distinguish between potential leads.
• Streamline lead optimization by efficiently processing large candidate libraries before committing to more intensive experimental or computational resources.

What the output means

The output provides a comprehensive set of results including the predicted 3D structure (in mmCIF or PDB format), confidence metrics like pLDDT and interface pTM scores, and quantitative binding predictions such as log-IC50 values and binary binding probabilities.

This output should be used to support scientific decision-making, helping to prioritize the most promising molecules for further study. It acts as a powerful computational assistant and does not replace experimental validation in the laboratory.

Why this matters

In the early stages of drug discovery, the bottleneck is often the sheer volume of candidate molecules that require evaluation. Traditional physics-based methods like FEP are highly accurate but computationally demanding, often requiring weeks of GPU time. Boltz-2 shifts this paradigm by providing near-FEP accuracy at speeds 1000× faster, effectively democratizing high-throughput structural and affinity prediction.

By providing a fully open-source model under an MIT license, Boltz-2 empowers researchers across academia and industry to conduct advanced structural biology and virtual screening without the barriers of proprietary software or massive hardware investments.

• Developed by: MIT researchers
• Source: Official GitHub repo
• Reference: Boltz-2 paper (bioRxiv 2025)