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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "6a7a5d41-e6d7-4efa-a481-3182963ca888",
"metadata": {},
"outputs": [],
"source": [
"!pip install gradio_client"
]
},
{
"cell_type": "markdown",
"id": "549b9b2c-3074-446b-962e-90c8efd2bd59",
"metadata": {},
"source": [
"# PLINDER inference and evaluation template API examples"
]
},
{
"cell_type": "markdown",
"id": "b979671e-97d6-4c52-bc6e-279a09d722c8",
"metadata": {},
"source": [
"## Run inference via predict endpoint"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "980771b6",
"metadata": {},
"outputs": [],
"source": [
"uri_inference = \"https://mlsb-blackhole-models-pli.hf.space/\"\n",
"uri_inference_busted = \"https://mlsb-blackhole-models-pli-busted.hf.space/\"\n",
"uri_inference_strongdocking = \"https://mlsb-strong-docking-baseline.hf.space/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c0171fa-ee2a-40b7-8578-aa8516b4ece9",
"metadata": {},
"outputs": [],
"source": [
"from gradio_client import Client, handle_file\n",
"from pathlib import Path\n",
"\n",
"\n",
"# If running docker container locally\n",
"dev_uri = \"http://localhost:7860/\"\n",
"client = Client(uri_inference)\n",
"\n",
"result = client.predict(\n",
" input_sequence=\"\",\n",
" input_ligand=\"CC(=O)N[C@H]1[C@H](O[C@H]2[C@H](O)[C@@H](NC(C)=O)CO[C@@H]2CO)O[C@H](CO)[C@@H](O)[C@@H]1O\",\n",
" input_msa=handle_file(\"./empty.a3m\"),\n",
" input_protein=handle_file(\"./input_protein_test.pdb\"),\n",
" api_name=\"/predict\",\n",
")\n",
"output_pdb, output_sdf, runtime = Path(result[0][0]), Path(result[0][1]), result[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c530fde1-7f57-4991-a53e-b3855657f9fc",
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"from rdkit import Chem\n",
"\n",
"local_dir = Path(\"./plinder-inference-outputs\")\n",
"local_dir.mkdir(exist_ok=True, parents=True)\n",
"\n",
"output_pdb = Path(shutil.copy(output_pdb, local_dir))\n",
"output_sdf = Path(shutil.copy(output_sdf, local_dir))\n",
"# save first ligand\n",
"ligand = Chem.SDMolSupplier(output_sdf)[0]\n",
"Chem.SDWriter(output_sdf).write(ligand)\n",
"\n",
"output_pdb, output_sdf"
]
},
{
"cell_type": "markdown",
"id": "b3c1c03e-74c1-4010-b385-e4366d43cd6f",
"metadata": {},
"source": [
"## Fetch evaluation metrics via evaluate endpoint"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "aa37fb5c",
"metadata": {},
"outputs": [],
"source": [
"uri_eval = \"https://ninjani-plinder-inference-template.hf.space/\"\n",
"client = Client(uri_eval)\n",
"result = client.predict(\n",
" system_id=\"4neh__1__1.B__1.H\",\n",
" receptor_file=handle_file(output_pdb),\n",
" ligand_file=handle_file(output_sdf),\n",
" api_name=\"/get_metrics\",\n",
" flexible=True,\n",
" posebusters=True,\n",
")\n",
"metrics, runtime = result\n",
"metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eef0d108-5d76-4bef-bd0c-4952d433ccaf",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"metric_df = pd.DataFrame(metrics[\"data\"], columns=metrics[\"headers\"])\n",
"metric_df.T"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a240815",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
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}
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