Use IntelliFold (AlphaFold3)

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

Overview

IntelliFold is a next-generation open-source foundation model for 3D biomolecular structure prediction, delivering AlphaFold3-level accuracy across proteins, nucleic acids, and small molecules. Through architectural innovations like a high-speed FlashAttentionPairBias kernel and a training-free, model-agnostic ranking method, IntFold achieves fast, accurate, and clash-free structure generation.

Neurosnap Overview

The IntelliFold (AlphaFold3) online webserver allows anybody with a Neurosnap account to run and access IntelliFold (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.

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Features

  • Custom FlashAttentionPairBias kernel for faster, memory-efficient inference on large biomolecular systems.
  • Model-agnostic, training-free ranking via structural self-consistency improves prediction quality.
  • High-accuracy all-atom prediction across proteins, nucleic acids, and small molecules.
  • Success rates matching AlphaFold 3 on FoldBench benchmark for protein and RNA complexes.

Statistics

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API Request

Access IntelliFold (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.

Advanced Settings

Choose the MSA database you want to use. Selecting "single_sequence" will result in the MSA containing only your target sequence. Note if you upload a custom MSA then your selection here will be ignored. In most cases mmseqs2_uniref_env tends to produce the best results. Warning: We query a 3rd party service to perform sequence searches, if you do not wish to share your sequence set this to "single_sequence".

Allowed Types: zip
Upload an optional ZIP file containing custom MSAs in FASTA (.fasta, .fa) or A3M (.a3m) format. Each MSA file must correspond to a sequence defined in the Input Sequences widget, and the first sequence in each MSA must be the one you want to fold. File names must follow this pattern: 'SEQUENCE_NAME.(a3m|fasta|fa)', where SEQUENCE_NAME matches the name of the input sequence. For example, to fold a dimer, include two files like 'sequence_1.fa' and 'sequence_2.a3m'.

Increased recycling steps tend to produce more accurate predictions but will also greatly increases prediction time. For smaller proteins and monomers we recommend 5 recycles, for bigger proteins we recommend 10 recycles.

The number of sampling steps to use for prediction.

The number of diffusion samples to use for prediction. This controls the number of output structures returned from the model.

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