How to Use Boltz-2 Online

Boltz-2: A Unified Model for Structure and Affinity Prediction

Boltz-2 (or Boltz2/Boltzdesign 2) is a next-generation biomolecular foundation model that marks a significant leap forward in computational drug discovery by jointly modeling complex structures and their binding affinities. This model builds on its predecessor, Boltz-1, and is presented as a strong open-source competitor to models like AlphaFold3. Its core innovation is a new affinity module that can predict how strongly a small molecule binds to its protein target.A major achievement of Boltz-2 is its ability to approach the accuracy of physics-based Free Energy Perturbation (FEP) methods for binding affinity prediction, while being over 1000 times faster. This speed and accuracy make large-scale virtual screening practical for early-stage drug discovery, addressing a critical bottleneck in the field.The model has been rigorously benchmarked and has demonstrated state-of-the-art performance. On the FEP+ benchmark, Boltz-2 achieves a Pearson correlation of 0.62, comparable to the industry-standard OpenFE pipeline. It also won the CASP16 affinity challenge, outperforming all other submitted methods. For structure prediction, Boltz-2 shows consistent gains over Boltz-1, with particular improvements in challenging modalities like DNA-protein complexes and antibody-antigen interactions.

How Boltz-2 Works

Boltz-2 extends the co-folding architecture of its predecessor, Boltz-1, to unify structure and binding affinity prediction in a single model. The model's architecture is a PairFormer network that predicts the 3D structures of molecular complexes including proteins, DNA, RNA, and ligands.

The key to Boltz-2's enhanced capabilities lies in several new features and upgrades to its training data:

• Unified Prediction: Unlike previous models that only predicted structure, Boltz-2 incorporates a new affinity module that predicts both the 3D binding pose and the binding affinity simultaneously. This module performs both classification (binder/non-binder) and regression of the affinity value.

• Curated Training Data: The model was trained on a massive, curated dataset of approximately 5 million binding affinity measurements from databases like PubChem and ChEMBL. The data was processed to handle variations in experimental protocols and extract a clear signal for training.

• Enhanced Controllability: Boltz-2 introduces features that give researchers more control over predictions. Users can specify constraints such as residue-pair distances or provide structural templates to guide the model. This allows for a more physically grounded and controllable design process.

• Physical Steering: The model integrates "physical steering" mechanisms to eliminate common structural artifacts like steric clashes and improper chirality, ensuring that the generated complexes are chemically plausible.

Boltz-2's predictions are delivered with confidence scores (derived from pLDDT and ipTM scores) and affinity values in a standardized format, allowing researchers to easily interpret the results and identify the most promising candidates.

What is Tamarind Bio?

Tamarind Bio is a pioneering no-code bioinformatics platform built to democratize access to powerful computational tools for life scientists and researchers. Recognizing that many cutting-edge machine learning models are often difficult to deploy and use, Tamarind provides an intuitive, web-based environment that completely abstracts away the complexities of high-performance computing, software dependencies, and command-line interfaces.

The platform is designed to be accessible to biologists, chemists, and other researchers who may not have a background in programming or cloud infrastructure. Key features include a user-friendly graphical interface for setting up and launching experiments, a robust API for integration into existing research pipelines, and an automated system for managing and scaling computational resources. By handling the technical heavy lifting, Tamarind empowers researchers to concentrate on their scientific questions and accelerate the pace of discovery.

Accelerating Discovery with Boltz-2 on Tamarind Bio

The integration of Boltz-2's advanced capabilities with Tamarind Bio's user-centric platform creates a powerful synergy that can significantly accelerate the drug discovery and research process.

• Speed and Accuracy: Boltz-2 provides FEP-level accuracy at an unmatched speed, and Tamarind Bio provides the scalable cloud infrastructure to run these predictions on a massive scale. This enables researchers to perform large-scale virtual screens that were previously infeasible, identifying promising drug candidates much faster.

• Streamlined Workflow: By abstracting away the computational and technical complexities of running Boltz-2 locally, Tamarind.bio allows researchers to focus on the science. The user-friendly interface and automated resource management drastically reduce the time spent on setup and troubleshooting, allowing for rapid iteration and a faster discovery pipeline.

• Enhanced Controllability: The ability to incorporate experimental data and user-defined constraints in Boltz-2 is amplified by Tamarind.bio's platform. This allows medicinal chemists and biologists to guide the model with their expertise and rapidly explore specific design hypotheses, making the entire process more efficient and effective.

How to Use Boltz-2 on Tamarind Bio

Tamarind.bio makes using Boltz-2 straightforward and efficient, regardless of your technical expertise. The no-code platform streamlines the entire workflow for molecular structure and binding affinity prediction.

Here is a simple, step-by-step guide for researchers to get started:

1. Access the Platform: Begin by logging in to the tamarind.bio website. The platform provides a central interface to access cutting-edge tools.

2. Select Boltz-2: From the list of available computational models, choose the Boltz-2 tool.

3. Specify Inputs: You will specify your protein by providing its sequence, and your ligand by providing its SMILES string or a file. The platform handles all necessary file conversions and preprocessing automatically.

4. Configure Parameters: In a simple, graphical user interface, you can specify your prediction parameters. You can choose to run a structural prediction or a binding affinity prediction, as well as set custom constraints or use a template to guide the model.

5. Submit and Monitor: With a single click, you can submit your job. The Tamarind.bio platform handles the allocation of powerful GPU resources and executes the Boltz-2 simulation, which can take as little as 20 seconds on a single GPU. You can monitor the progress of your job directly from a user-friendly dashboard.

6. Analyze the Results: Once the job is complete, you will receive a comprehensive report. The results include a ranked list of predicted structures, binding affinity values (log(IC50)), and confidence scores. You can explore interactive 3D visualizations of the structures directly in your browser, enabling you to inspect the predicted binding poses and evaluate the affinity predictions.