AbSeek™ Integrates Chai-1 for Full-Scenario Molecular Structure Prediction

Industry Insight

In antibody drug discovery and structural biology, complex structure prediction is moving beyond single-protein modeling toward multi-molecular systems. Researchers need to understand not only the structure of an antibody or target protein, but also interface recognition, conformational changes, and binding modes in antibody–antigen, protein–ligand, and protein–nucleic acid complexes.

Traditional structure modeling workflows often depend on docking, template search, multiple sequence alignment, and multi-step optimization. These workflows can be complex and require substantial domain expertise. For teams that need to compare antibody candidates, evaluate mutation effects, or design validation experiments, faster, more intuitive, and more iterative structure prediction tools are becoming an important driver of R&D efficiency.

The ability to complete sequence input, complex structure generation, experimental restraint integration, and visual evaluation within one platform is a key step toward industrial adoption of AI-powered structure prediction.

Introduction

Cyagen’s AbSeek^TM intelligent antibody computing platform has officially integrated Chai-1, a multimodal foundation model for molecular structure prediction developed by the Chai Discovery team.

Chai-1 can perform protein–protein, antibody–antigen, and protein–small molecule complex structure prediction in single-sequence mode, helping researchers obtain structural insights in a more direct workflow. It turns complex structure modeling into an operational and iterative intelligent module that supports structural analysis and experimental design in antibody drug discovery.

This deployment marks another step for AbSeek^TM toward AI-driven complex structure prediction, providing new computational capabilities for systematic and accelerated molecular discovery.

Figure 1. Chai-1 tool page on AbSeek^TM

What Is Chai-1?

Chai-1 is a multimodal foundation model for molecular structure prediction. It can infer three-dimensional biomolecular structures and interaction modes from sequence information, chemical structure descriptions, and experimental restraints.

Chai-1 extends the technical direction of the AlphaFold model family. In addition to monomer prediction, it can handle multi-molecular complexes, including protein–protein, protein–small molecule, and protein–nucleic acid interactions.

In benchmark results reported in public technical materials, Chai-1 has shown competitive performance across multiple standard datasets:

• On the PoseBusters protein–small molecule docking set, the success rate reaches 77–81%, approaching AlphaFold3-level performance;
• For protein–protein complex prediction, the success rate (DockQ > 0.23) reaches 0.751, compared with 0.677 for AlphaFold-Multimer 2.3;
• For antibody–antigen complex prediction, the success rate reaches 0.529, representing a clear improvement over traditional approaches.

Chai-1 is not only an algorithmic advance. It also helps move structure prediction from complex and time-consuming computational workflows toward flexible, intuitive tools that can be integrated into day-to-day experimental design.

What Changes After Chai-1 Is Integrated into AbSeek^TM?

The integration of Chai-1 is a system-level upgrade. It expands the computational capabilities of AbSeek^TM and further optimizes the workflow for intelligent structure prediction.

1. From Docking to End-to-End Prediction

In traditional antibody–antigen complex modeling, researchers often rely on docking, template matching, or multi-step optimization. These approaches can be sensitive to input conditions and may have limited ability to capture conformational changes in complexes.

With Chai-1, users can directly input antibody and antigen sequences and generate complex structures end to end. Prediction can be completed without complex multiple sequence alignment (MSA) workflows or template screening.

Reported benchmark results show that:

• the success rate for antibody–protein complex prediction (DockQ > 0.23) reaches 0.529, compared with 0.380 for AF2.3;
• in single-sequence mode, the success rate remains 0.479, outperforming AlphaFold-Multimer with MSA in the reported comparison;
• on the protein–small molecule PoseBusters task, Chai-1 reaches a 77–81% success rate, making it comparable with AlphaFold3 in this setting.

For platform users, this means:

• Faster modeling cycles: the workflow from sequence to structure can be substantially simplified, supporting faster project iteration;
• Stronger complex modeling capability: the model can be applied to antibody–antigen, peptide–protein, and small molecule–protein systems.

The introduction of Chai-1 further enables sequence-driven automated structure modeling on the platform, representing an important capability upgrade for intelligent antibody computing.

Figure 2. Model performance of Chai-1

2. Integration with Experimental Data for More Realistic Predictions

In antibody design and validation, experimental information is often critical for improving prediction reliability. A key advantage of Chai-1 is its ability to incorporate experimental restraints. With these soft restraints, the model can adjust predicted structures to better match realistic conformations.

Reported results show that:

• when a single antibody–antigen distance restraint is provided, the DockQ success rate increases from 35% to 57%;
• when four epitope residue constraints are provided, the DockQ success rate more than doubles.

This data-integrated prediction approach enables a tighter feedback loop between experiment and computation: experiments provide structural restraints, the model generates more reliable structures, and the resulting structures inform the next round of experimental design.

On AbSeek^TM, users can interactively enter restraint information through the interface. Without manually building complex input files, they can call Chai-1’s restraint-aware inference module to perform conditional structure prediction.

Figure 3. Chai-1 success rate in antibody–antigen complex structure prediction

3. Support for Multiple Molecular Types Across R&D Scenarios

Chai-1 is not limited to protein–protein systems. It is designed with cross-modal learning capabilities and can process multiple types of molecular combinations:

• protein–small molecule complexes, such as enzyme–inhibitor and protein–ligand systems;
• protein–DNA/RNA complexes, such as transcription factor–DNA and mRNA–protein interactions;
• complex systems containing chemical modifications, metal ions, or ligands.

This broad applicability is especially important in drug discovery. Researchers can use one model framework across antibody screening, ligand optimization, and structural validation without frequently switching between different algorithms or toolchains.

The platform also connects prediction results with visualization and evaluation modules. In the structure visualization interface, pLDDT can be represented with different colors to help users inspect regional prediction confidence. Aggregate 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 Chai-1?

The deployment of Chai-1 provides targeted structural computing support for different types of R&D teams.

1. Biopharmaceutical Companies

• Accelerating early-stage R&D and candidate screening: the workflow from sequence to complex structure generation is simplified, enabling rapid assessment of candidate antibody binding characteristics.
• Supporting mutation and affinity analysis: structural analysis can help evaluate mutation effects and guide affinity optimization or immune escape risk assessment.
• Improving project transparency: visual interfaces and automated analysis reports make structure prediction results easier to integrate into R&D workflows.

2. Research Institutes and Academic Laboratories

• Lowering the barrier to structure prediction: prediction tasks can be completed through a graphical interface without complex command-line operations.
• Enhancing experiment–computation coordination: users can input epitope, cross-linking, disulfide bond, or related restraints to obtain predictions closer to experimental hypotheses.
• Expanding research scope: the model supports structural modeling for atypical sequences such as synthetic peptides and chimeric antibodies, providing more flexibility for exploratory research.

AbSeek^TM Connects Sequence, Structure, Prediction, and Experiment

The deployment of Chai-1 further enables AbSeek^TM to integrate the full workflow from sequence to structure and from prediction to experiment. Researchers can now complete data input, structure modeling, and result analysis within a unified environment.

Looking ahead, the platform will continue expanding its intelligent capabilities:

• integrating more frontier models and in-house algorithms to further improve antibody–antigen interface prediction;
• optimizing the integration of experimental data so models can better understand constraints in real biological systems;
• advancing industrial applications of AI structure prediction in drug discovery, antibody optimization, and molecular design.

Try it now: In the antibody structure prediction module of AbSeek^TM, select Chai-1, upload sequence or complex information, and generate structure prediction results.

Open collaboration: We welcome research institutions and industry partners to explore innovative applications of Chai-1 in antibody R&D, structural biology, and multimodal molecular modeling.

With intelligent computing, we aim to connect molecular structure with biological function and make structure prediction a core engine for accelerating life science innovation.