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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 a strong competitor with AlphaFold3. Its core innovation is a new affinity module that can predict how strongly a small molecule binds to its protein target. Boltz-2 has the 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 within a single model. The model's architecture is built on a PairFormer network which predicts the 3D structures of molecular complexes including proteins, DNA, RNA, and ligands.
The power in Boltz-2's enhanced capabilities lies in several new and upgraded features:
Unified Prediction: Unlike previous models, which only predicted structure, Boltz-2 incorporates a new affinity module predicting both the 3D binding pose and the binding affinity simultaneously. The module performs both classification (binder/non-binder) and regression of the affinity value.
Curated Training Data: Training of the model occurred on a massive, curated dataset of ~5 million binding affinity measurements from databases such as PubChem and ChEMBL. The data was designed & processed to handle variations in experimental protocols and extract a clear signal for training.
Enhanced Controllability: Boltz-2 excels in giving researchers a lot more granular control over model predictions. You have the ability to specify constraints, such as residue-pair distances or provide structural templates, to guide the model, allowing for a more physically grounded and controllable design process.
Physical Steering: The model integrates "physical steering" mechanisms to eliminate common structural artifacts such as 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 provide easy access to biologists, chemists, and other researchers who may not have a background in programming or cloud infrastructure but want to run experimental models with their data. 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, significantly accelerating 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 researchers 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:
Access our Platform: Begin by logging in to the tamarind.bio website.
Select Boltz-2: From the list of tiles, either search or select Boltz-2.
Specify Inputs: Once you are in the Boltz-2 tool, you will next want to specify your protein by providing its sequence, and your ligand (SMILES string or a file). Any necessary file conversions and preprocessing are carried out automatically.
Configure Parameters: In a simple, graphical user interface, you are able to specify all of 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.
Submit and Monitor: In order to submit your job, click "Submit" and your job will start running. The Tamarind Bio platform handles the allocation of powerful GPU resources and will perform 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 the dashboard.
Analyze the Results: After completion of the job, you will receive a comprehensive report within the Tamarind application. The results include a ranked list of predicted structures, binding affinity values (log(IC50)), and confidence scores. From this data, you can explore interactive 3D visualizations of the structures, enabling you to inspect the predicted binding poses and evaluate the affinity predictions.