Use Boltz-1 (AlphaFold3)

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Protein Folding Molecular Docking

Overview

Boltz-1 is an open-source deep learning model achieving AlphaFold3-level accuracy in predicting the 3D structures of biomolecular complexes. Incorporating innovations in model architecture, data processing, and confidence prediction, Boltz-1 democratizes access to state-of-the-art tools for modeling biomolecular interactions. Released under the MIT license, Boltz-1 empowers researchers with comprehensive training code, model weights, and datasets to accelerate discoveries in drug design and structural biology.

Neurosnap Overview

The Boltz-1 (AlphaFold3) online webserver allows anybody with a Neurosnap account to run and access Boltz-1 (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

  • Achieves AlphaFold3-level accuracy for predicting 3D biomolecular complex structures.
  • Supports diverse biomolecular systems, including proteins, nucleic acids, and small molecules.
  • Innovative algorithms for MSA pairing, pocket-conditioning, and unified cropping.
  • Optimized confidence model for reliable biomolecular interaction predictions.
  • Significant speed and computational efficiency improvements over comparable models.
  • Extensive benchmark validation on CASP15 and curated test sets.

Statistics

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API Request

Access Boltz-1 (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.

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 any residue modifications for amino acid, dna, or rna sequences. The format for residue modifications is SEQ_ID:RESIDUE_INDEX:CCD_CODE per line.


SEQ_ID: This is the title assigned to the sequence within the Input Sequences input.

RESIDUE_INDEX: This is the residue index / position of the amino acid you want to modify. Use a residue ID of 1 for the first residue in your sequence.

CCD_CODE: This is the CCD code of the modification you want to assign to the residue.

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".

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.

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