RFdiffusion NIM endpoints
The model provides endpoints as described below. Input and output parameters correspond to properties in a JSON object submitted to or received from an endpoint.
Endpoint path: biology/ipd/rfdiffusion/generate
Input parameters
input_pdb (string, null): Optional. This is an input PDB (Protein Data Bank) file: protein chains and amino acids from this file are used to select binder target and motifs.input_pdb_asset (string, null): Optional. Optional pre-uploaded NVCF Asset ID. If using this field, original file name should be provided via input_pdb argument.contigs (string): Required. Historically, contigs stands for ‘contiguous [protein regions]’. This string defines a protein that is being generated. It is a specification written in a domain-specific language that tells RFdiffusion which part of the input protein are to be kept and what kind of a binder (or a scaffold) needs to be constructed. As an example, a string ‘A10-100/0 50-150’ instructs RFdiffusion to keep amino acids 10-100 in Chain A [from the input PDB file], then break the chain (special ‘/0’ notation, which signifies the end of the chain and thus effectively makes ‘A10-100’ a new target protein), and construct a new chain (effectively a binder protein) of length 50 to 150 amino acids.hotspot_res (array, null): Optional. The hotspot residues string provides a way to specify which region the new protein (binder) must contact with the original input protein (a target), therefore we can guide a binder to a specific region. In UI, the format of the string is a comma-separated list of amino acids present in the input PDB file, e.g. ‘A50,A51,A52’ specifies three hotspot residues in the Chain A, at positions 50, 51 and 52. Note that in API, however, the amino acids are specified as a list of strings, e.g. [‘A50’, ‘A51’, …].diffusion_steps (integer, null): Optional. Default is 15. RFdiffusion is a diffusion generative model, it was trained by diffusing (adding noise) to a training data set. The generative process works by reversing the time steps (i.e. denoising): starting from randomly placed atoms, and reverse-diffusing the positions to arrive at a probable atom positions. The diffusion steps parameter tells RFdiffusion how many steps it will run the denoising process for. 15 is the minimum, 50 is the default.random_seed (integer, null): Optional. RFdiffusion is a generative model, its function is to generate novel and diverse proteins. Setting random seed allows to turn RFdiffusion into a deterministic model, where an input protein, a task and a fixed seed would always produce the same output. This argument is useful for development purposes, but otherwise should be unset.
Outputs
output_pdb (string): Required. Output protein in PDB format.elapsed_ms (integer): Required. Elapsed time on server side.
Endpoint path: v1/health/ready
Input parameters
None.
Outputs
The output of the endpoint is a JSON response containing a value indicating the
readiness of the microservice. When NIM is ready, following
response is returned: {"status":"ready"}.