Use OpenFold3 (AlphaFold3)

Official Neurosnap webserver for accessing OpenFold3 (AlphaFold3) online.

Overview

OpenFold3 (OF3) is an open-source, bitwise reproduction of AlphaFold3 that delivers near-parity performance for comprehensive biomolecular structure prediction across proteins, nucleic acids, and protein–ligand complexes. Developed by a consortium including Columbia University, Lawrence Livermore National Laboratory, Novo Nordisk, AWS, Seoul National University, the Chan Zuckerberg Initiative, Absci, SandboxAQ, NVIDIA, and others, OF3 replicates the diffusion-based multimodal architecture of AlphaFold3 while incorporating stability and correctness improvements. Key refinements include updated LayerNorm handling, corrected inter-chain masking logic, revised distance binning, and updated sequence and structure databases. Benchmarks across CASP16, FoldBench, and Runs N’ Poses demonstrate performance within experimental error of AlphaFold3 on RNA and monomeric proteins, and competitive results on multimeric and ligand-bound complexes. OpenFold3 is fully open-source with inference and training code, model weights, and documentation to support community-driven research in structural biology and drug discovery.

Neurosnap Overview

The OpenFold3 (AlphaFold3) online webserver allows anybody with a Neurosnap account to run and access OpenFold3 (AlphaFold3), no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.

View Paper

Features

  • Implements AlphaFold3-compatible diffusion-based architecture for multimodal structure prediction.
  • Supports proteins, RNA, and small-molecule complexes through unified pair representations.
  • Improved normalization, masking, and binning logic for stable and accurate training.
  • Benchmarked on CASP16, FoldBench, and Runs N’ Poses datasets, matching or approaching AF3 performance.
  • MSAs are generated by our internal private MSA server.

Statistics

Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.

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API Request

Access OpenFold3 (AlphaFold3) using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.

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Job Note

Provide a name or description for your job to help you organize and track its results. This input is solely for organizational purposes and does not impact the outcome of the job.

Configuration & Options

Service Inputs

The amino acid, DNA, and RNA sequences corresponding to the molecules you want to predict. For complex prediction simply provide multiple sequences in this input and they will all be folded at the same time together. For DNA and RNA sequences enter them in the 5′-->3′ direction. For DNA do not include the complementary sequence as they are automatically generated.

Input small molecules to include in prediction. All inputs are converted to SMILES format, with SMILES strings being the preferred input for optimal results.

Residue Modifications Instructions

Specify residue modifications for amino acid, DNA, or RNA sequences. Each row should define one modification, with values separated by colons (:). The format is:

SEQ_ID : RESIDUE_INDEX : CCD_CODE
  • SEQ_ID → The name of the sequence as defined in Input Sequences.
  • RESIDUE_INDEX → The residue position to modify. Use 1 for the first residue.
  • CCD_CODE → The Chemical Component Dictionary (CCD) code of the modification.


🔹 Example Input:

protein_1:102:MLY
dna_1:1:5MC
rna_1:26:PSU


⚠️ Notes:

See above instructions for information on how to use this input.

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Review your configuration, then confirm the estimated credit cost before you run the job.

Note that credit estimates are not guaranteed and runtime can vary depending on inputs and settings.

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