update notebook

api-template.ipynb

@@ -26,6 +26,18 @@
     "## 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,
@@ -36,19 +48,19 @@
     "from gradio_client import Client, handle_file\n",
     "from pathlib import Path\n",
     "\n",
-    "uri = \"https://ninjani-plinder-inference-template.hf.space/\"\n",
+    "\n",
     "# If running docker container locally\n",
     "dev_uri = \"http://localhost:7860/\"\n",
-    "client = Client(uri)\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=None, # optional in this implementation\n",
-    "    input_protein=handle_file(\"./input_protein_test.cif\"),\n",
-    "    api_name=\"/predict\"\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]\n",
-    "print(output_pdb, output_sdf, runtime)\n"
+    "output_pdb, output_sdf, runtime = Path(result[0][0]), Path(result[0][1]), result[-1]"
    ]
   },
   {
@@ -59,12 +71,16 @@
    "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"
    ]
@@ -79,35 +95,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e5e26250-f20d-484d-84e2-320cdfef830a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client = Client(uri)\n",
-    "result = client.predict(\n",
-    "    system_id=\"4neh__1__1.B__1.H\",\n",
-    "    receptor_file=handle_file(\"./input_protein_test.cif\"),\n",
-    "    ligand_file=handle_file(\"./input_ligand_test.sdf\"),\n",
-    "    api_name=\"/get_metrics\"\n",
-    ")\n",
-    "metrics, runtime = result\n",
-    "metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "aa37fb5c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "client = Client(uri)\n",
+    "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",
+    "    api_name=\"/get_metrics\",\n",
+    "    flexible=False,\n",
+    "    posebusters=True,\n",
     ")\n",
     "metrics, runtime = result\n",
     "metrics"
@@ -123,8 +124,16 @@
     "import pandas as pd\n",
     "\n",
     "metric_df = pd.DataFrame(metrics[\"data\"], columns=metrics[\"headers\"])\n",
-    "metric_df"
+    "metric_df.T"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a240815",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {