How to Use Chai-1 (AlphaFold3)

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Structure Prediction & Folding Co-Folding Conformation Generation Nucleotides Protein Folding Proteins Protein–Ligand Docking Protein–Protein Docking Small Molecules

Use Chai-1 (AlphaFold3) online for open multimodal biomolecular structure prediction and confidence-guided complex analysis.

Chai-1 is an open multimodal biomolecular structure prediction model designed for proteins, nucleic acids, ligands, and mixed complexes. The technical report positions it as an AlphaFold3-class system with strong performance across interaction benchmarks, while also supporting a competitive single-sequence workflow when a deep MSA is unavailable or undesirable.

On Neurosnap, researchers can combine Input Sequences, Input Molecules, and optional Restraints to study protein-protein, protein-nucleic-acid, and protein-ligand assemblies in a no-code workflow. The results are organized around confidence interpretation rather than a single top pose, with pLDDT, PAE, PDE, ipTM, and additional interface metrics such as ipSAE, LIS, pDockQ, and pDockQ2 that help decide whether a complex is globally credible or only locally trustworthy.

How Chai-1 (AlphaFold3) Works

Chai-1 uses an AlphaFold3-class all-atom architecture for multimodal complex prediction, but the paper emphasizes two practical strengths for research workflows: broad conditioning across molecular types and a strong single-sequence mode powered by language-model representations. When evolutionary information is available, MSA pairing can still be used; when it is not, Chai-1 can often retain useful complex accuracy without the alignment-generation overhead.

On Neurosnap, MSA Mode, Number Recycles, Diffusion Steps, and Diffusion Samples expose the main decisions researchers make when balancing speed, diversity, and confidence. Optional residue-level restraints are useful when experiments already suggest an interface or contact pattern.

In practice, Chai-1 is best used as a hypothesis-ranking system rather than a black-box oracle. Researchers typically compare models using per-residue confidence, chain-pair ipTM, inter-chain ipSAE, LIS, pDockQ, and pDockQ2 to determine whether a predicted assembly is ready for docking follow-up, mutagenesis planning, or experimental validation.

What is Neurosnap?

Neurosnap is the leading platform for bioinformatics and computational science focused on expanding access to powerful modeling and simulation tools. Because many state-of-the-art machine learning systems remain complex to install, configure, and scale, Neurosnap offers a clean, browser-based workspace that removes the burden of infrastructure management, dependency conflicts, and command-line tooling.

Built for biologists, chemists, and cross-disciplinary scientists, the platform enables advanced computational workflows without requiring expertise in software engineering or cloud architecture. Researchers can launch analyses through an intuitive interface, connect programmatically through a comprehensive API, and rely on automated resource management to scale workloads efficiently. By taking care of the underlying compute and operational complexity, Neurosnap allows teams to devote their energy to scientific progress and faster iteration. Security and data protection remain foundational principles, with clear safeguards outlined in our Terms of Use and Privacy Policy to ensure your work stays protected.

Advancing Discovery with Chai-1 (AlphaFold3) on Neurosnap

Using Chai-1 (AlphaFold3) on Neurosnap could drastically accelerate open multimodal structure prediction and interface-confidence triage from sequences, nucleic acids, and ligands.

  • Study-fit inputs: Chai-1 accepts proteins, DNA, RNA, ligands, and optional restraints in one run, which matches real multimolecule structure questions.
  • Flexible evidence use: MSA Mode lets researchers choose between mmseqs2-based evolutionary context and a faster single-sequence workflow when privacy or turnaround matters.
  • Decision-ready confidence: pLDDT, PAE, PDE, ipTM, ipSAE, LIS, pDockQ, and pDockQ2 make it easier to separate plausible assemblies from uncertain poses.
  • Neurosnap workflow: The browser-based interface removes local GPU setup while keeping the key scientific controls visible.

How to Use Chai-1 (AlphaFold3) on Neurosnap

To harness the capabilities of Chai-1 (AlphaFold3), researchers can follow this streamlined workflow within Neurosnap:

  1. Access Neurosnap: Start by logging in to the Neurosnap website.
  2. Select Tool: From the list of available tools, choose Chai-1 (AlphaFold3).
  3. Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
  4. Run Tool: Submit the Chai-1 (AlphaFold3) job and Neurosnap will execute it in the cloud, automatically notifying you as soon as your results are ready.
  5. Review Output: Explore your results through rich visualizations, including figures, plots, and interactive views designed to help you analyze findings with clarity and confidence.

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