AbSeek™ Integrates Boltz-2: Toward Physics-Guided, Multimodal Structure Prediction

Industry Insight

In antibody drug discovery, structural biology, and AI for Science workflows, structure prediction is moving beyond the generation of a single static model. Researchers increasingly need controllable, interpretable, and experimentally aware predictions that can reflect interface contacts, molecular flexibility, experimental restraints, and physical plausibility.

Traditional workflows often rely on template search, docking, iterative physics-based refinement, and manual inspection. For complex multi-chain systems, this can lead to fragmented inputs, long turnaround times, and substantial post-processing effort. Models that can integrate sequence information, experimental signals, and physical constraints within a unified framework are becoming a critical infrastructure for antibody discovery, protein design, and molecular assembly studies.

Introduction

To address these evolving needs, the AbSeek^TM intelligent computing platform has officially integrated Boltz-2, a foundation model for structural biology jointly developed by MIT CSAIL and Valence Labs.

Boltz-2 is the latest generation of the Boltz model family. It introduces important advances in multi-molecular structure prediction: it improves modeling accuracy for protein–protein, antibody–antigen, and protein–nucleic acid complexes, while incorporating controllable modeling and multi-modal integration to make predicted structures more aligned with biophysical reality.

This deployment marks AbSeek^TM’s transition toward physics-guided structure generation. The model is designed not only to predict structures, but also to account for structural dynamics and restraints under the guidance of experimental data and physical constraints, providing a more reliable structural foundation for antibody drug discovery.

Figure 1. Boltz-2 tool page on AbSeek^TM

What Is Boltz-2?

Boltz-2 is a multimodal and controllable foundation model for biomolecular structure prediction. It can generate high-accuracy, physically plausible three-dimensional structures by incorporating sequence information, experimental data, and physical restraints.

Architecturally, Boltz-2 builds on the co-folding concepts of AlphaFold and Boltz-1, while introducing three core innovations:

• Method Conditioning: The model can adapt its structural distribution according to different experimental modalities, such as X-ray crystallography, NMR, and molecular dynamics (MD), making predictions more consistent with the source data.
• Template & Distance Conditioning: The model supports multi-chain template inputs and distance restraints, enabling integration of epitope, cross-linking, or experimental distance information.
• Boltz-Steering: During inference, the model automatically applies physics-based potential restraints to reduce steric clashes and chemical errors, generating structures that are closer to experimental usability.

In public benchmarks, Boltz-2 has shown leading performance across multiple complex systems:

• PDB multimodal sets: overall structural accuracy is higher than Boltz-1 and on par with AlphaFold-3;
• Antibody–antigen complexes: prediction success rates are clearly improved, with strong DockQ performance even in template-free settings;
• Protein–DNA/RNA models: large-scale distillation training brings notable gains for nucleic-acid-binding systems.

Boltz-2 shifts structure prediction from static reconstruction toward dynamic understanding, advancing physically consistent modeling from sequence to structure.

What Changes After Boltz-2 Is Integrated into AbSeek^TM?

1. From Single Prediction to Controllable Generation

In conventional modeling workflows, researchers often rely on docking, template matching, or multi-step optimization. These processes can be time-consuming and sensitive to input conditions. With Boltz-2, the workflow becomes more direct: users can input antibody and antigen sequences and define distance restraints, pocket restraints, or atomic covalent bond restraints as needed.

These controllable inputs allow researchers to guide the model toward the desired structural hypothesis, turning structure prediction from a passive output into an active design process informed by experimental assumptions.

2. Dynamic Structure Prediction: Capturing Molecular Motion

One of the most important advances in Boltz-2 is its ability to predict conformational diversity. Through method-conditioned training on molecular dynamics (MD) data, the model can learn local protein flexibility and global conformational changes.

On the mdCATH and ATLAS dynamics datasets:

• Boltz-2 achieves RMSF correlations above 0.8, reflecting stronger agreement between local dynamics and simulation-derived behavior;
• prediction diversity is substantially improved, enabling the generation of multiple conformational states for complex structures;
• the model better captures breathing-like dynamics while maintaining structural accuracy.

As a result, platform users can move beyond a single static structure and obtain conformational ensembles under different energy states. This can support studies of antibody plasticity, epitope exposure, and conformational variation.

Figure 2. Model performance of Boltz-2

3. Higher Physical Consistency with Boltz-Steering

A useful predicted structure must not only look correct; it must also be physically plausible. Boltz-2 integrates the Boltz-Steering inference mechanism, which introduces molecular-potential-based corrections during generation to improve stereochemical quality.

According to reported experimental results:

• Boltz-Steering can reduce steric clashes by approximately 70%;
• for small-molecule complex structures, geometry completeness and chirality retention approach experimental crystal standards;
• in multi-chain systems, it helps avoid interface penetration and false contacts.

This mechanism makes model outputs easier to pass into downstream energy minimization or molecular dynamics analysis, reducing the burden of post-processing.

Figure 3. Model architecture of Boltz-2

4. Full-Modal Complex Support for Broader Research Scenarios

Boltz-2 is trained across diverse molecular types and interaction patterns, enabling the platform to support multiple structure prediction tasks within a unified framework:

• Antibody–antigen complexes: direct modeling from sequence inputs to complete complex structures;
• Protein–small molecule and protein–peptide complexes: generation of docking sites without strict reliance on templates;
• Protein–DNA/RNA complexes: support for predicting nucleic acid recognition and binding conformations.

The platform also connects prediction results with visualization and evaluation modules. In the structure visualization interface, pLDDT can be represented with distinct colors to help users inspect regional prediction quality. Confidence score, PTM, iPTM, and related metrics are also summarized to support rapid result screening and confidence assessment.

Figure 4. Visualization module

Who Can Benefit from Boltz-2?

1. Biopharmaceutical Companies

• Faster early-stage modeling: single-complex prediction can be completed within 10 minutes, supporting more efficient candidate structure screening.
• Direct integration of experimental data: cross-linking or epitope restraint information can be incorporated during antigen–antibody screening.
• Improved interpretability: physically consistent structures can support pharmacological modeling and design validation.

2. Research Institutes and Academic Laboratories

• Lower modeling barrier: predictions can be completed through a graphical interface without programming.
• Support for exploratory research: suitable for studies involving novel antibody formats, chimeric proteins, or nucleic-acid-binding proteins.
• Expanded structural hypothesis testing: experimental restraints can be combined with structure generation to rapidly evaluate conformational hypotheses.

From Sequence-Driven to Physics-Guided Structure Prediction

The integration of Boltz-2 enables AbSeek^TM to move from sequence-driven structure prediction toward physics-guided structure generation. Researchers can work in one environment from sequence input and restraint definition to structure generation and dynamic analysis.

Looking ahead, the platform will continue to advance in the following directions:

• integrating more open foundation models to strengthen multimodal structure fusion;
• improving coordination with experimental restraints so that predictions better reflect realistic conformational distributions;
• exploring AI-driven structure–function relationship modeling to accelerate antibody discovery and protein design.

Try it now: In the molecular structure prediction module of AbSeek^TM, choose Boltz-2, upload sequence or complex information, and generate multi-conformational, physically consistent 3D structures.

Open collaboration: We welcome research institutions and industry partners to explore innovative applications of Boltz-2 in antibody engineering, protein dynamics, and molecular assembly prediction.

With intelligent computing, we aim to better understand the beauty of molecular structures and make AI-based structure prediction a core engine for life science innovation.