Use Chai-1 (AlphaFold3)

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. To specify modified amino acids that already have an associated CCD code, include the modified residue's CCD code in parentheses directly in the sequence in place of its canonical residue, e.g., "RKDES(MSE)EES" to specify a selenomethionine at the 6th position.

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

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

Choose whether you want to use an MSA generated using mmseqs2 or if you want to use single sequence mode which leverages language model embeddings instead ("single_sequence"). The single sequence approach is much faster and achieves 90% of the accuracy of its MSA counterpart. 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 6 recycles, for bigger proteins we recommend 10 recycles.

The number of diffusion steps to use for prediction.