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NVIDIA logoBioNeMo OpenFold3

NVIDIA NIM for OpenFold3 diffusion-based structure prediction.

Model details

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Example usage

Call BioNeMo OpenFold3 on Baseten using the NIM structure prediction route.

Note: You need to procure your own NVAIE licenses to access this NIM.

Input
1import os
2import requests
3
4MODEL_ID = os.environ["BASETEN_MODEL_ID"]
5BASETEN_API_KEY = os.environ["BASETEN_API_KEY"]
6BASETEN_URL = (
7    f"https://model-{MODEL_ID}.api.baseten.co/environments/production/sync"
8    "/biology/openfold/openfold3/predict"
9)
10
11sequence = "LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"
12minimal_msa = f">query\n{sequence}\n"
13
14payload = {
15    "inputs": [
16        {
17            "input_id": "villin_hp35_test",
18            "molecules": [
19                {
20                    "type": "protein",
21                    "sequence": sequence,
22                    "msa": {
23                        "main": {
24                            "a3m": {
25                                "alignment": minimal_msa,
26                                "format": "a3m",
27                            }
28                        }
29                    },
30                }
31            ],
32            "diffusion_samples": 1,
33            "output_format": "pdb",
34        }
35    ]
36}
37
38response = requests.post(
39    BASETEN_URL,
40    headers={
41        "Authorization": f"Bearer {BASETEN_API_KEY}",
42        "Content-Type": "application/json",
43    },
44    json=payload,
45    timeout=300,
46)
47response.raise_for_status()
48result = response.json()
49print(result)
50
JSON output
1{
2    "outputs": [
3        {
4            "input_id": "villin_hp35_test",
5            "structures_with_scores": [
6                {
7                    "structure": "<PDB text>",
8                    "format": "pdb",
9                    "confidence_score": 0.85
10                }
11            ]
12        }
13    ]
14}

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