Fix issue-driven docs and notebook inference flows

Author: theproteinbot

README.md

@@ -97,6 +97,8 @@ pip install -e .
 conda install -c kimlab stride
 ```
 
+Use the explicit `-c kimlab` form above. The `kimlab::stride` shorthand can fail on some conda setups.
+
 ### 🐳 Docker
 
 A `Dockerfile` is included for containerized usage (PyTorch 2.4, CUDA 12.4, Python 3.12.4 via Mambaforge).

batch_demo.ipynb

@@ -1 +1,57 @@
-{"cells":[{"cell_type":"markdown","id":"49b08bbc-db31-4aa4-89b4-2313c0030642","metadata":{},"source":"## Define inputs"},{"cell_type":"code","execution_count":11,"id":"3c1841bb-8c35-4562-b86f-30777bb299bb","metadata":{},"outputs":[],"source":"parameter = 'Km' # allowed values: [\"kcat\", \"Km\", \"Ki\"] \nparameter = parameter.lower()\n\nuse_cpu = 1 # set to 0 if you have GPU enabled\n\nuniprot_id = \"P35557\" \n# If you do not have a uniprot-id, enter some name (for eg: \"enzyme1\")\nsequence = 'MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ'\nSMILES = \"C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O\" "},{"cell_type":"markdown","id":"db40ad26-0481-405b-8323-70d23f0ef44e","metadata":{},"source":"## Navigate to below cell and click \"Run->Run Selected Cell\" to get prediction"},{"cell_type":"markdown","id":"81de60cf-39b5-4546-83da-497c15e01b59","metadata":{},"source":"The result will be printed on the right column"},{"cell_type":"code","execution_count":16,"id":"be6cfa1f-9724-44ad-8ff9-ba8dcd51b887","metadata":{"jupyter":{"source_hidden":true}},"outputs":[{"name":"stdout","output_type":"stream","text":["Input success!\n","Enzyme sequence length: 465\n","Substrate structure:\n","Predicting.. This will take a while..\n","\n"]},{"data":{"text/latex":["$\\displaystyle K_{m} = 5.57977 mM$"],"text/plain":["<IPython.core.display.Math object>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","\n"]},{"data":{"text/latex":["$\\displaystyle log_{10}(K_{m}) = 0.74662$"],"text/plain":["<IPython.core.display.Math object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/latex":["$\\displaystyle SD_{total} = 0.69207$"],"text/plain":["<IPython.core.display.Math object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/latex":["$\\displaystyle SD_{aleatoric} = 0.64138$"],"text/plain":["<IPython.core.display.Math object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/latex":["$\\displaystyle SD_{epistemic} = 0.25999$"],"text/plain":["<IPython.core.display.Math object>"]},"metadata":{},"output_type":"display_data"}],"source":"import time\nimport os\nimport pandas as pd\nimport numpy as np\nfrom IPython.display import Image, display\nfrom rdkit import Chem\nfrom IPython.display import display, Latex, Math\n\ndef create_csv_sh(parameter, uni, seq, smi):\n  try:\n    mol = Chem.MolFromSmiles(smi)\n    smi = Chem.MolToSmiles(mol)\n  except:\n    print('Invalid SMILES input!')\n    print('Correct your input! Exiting..')\n    return\n  valid_aas = list('ACDEFGHIKLMNPQRSTVWY')\n  for aa in seq:\n    if not aa in valid_aas:\n      print('Invalid Enzyme sequence input!')\n      print('Correct your input! Exiting..')\n      return\n  if parameter=='kcat':\n    if '.' in smi:\n      x = smi.split('.')\n      y = sorted(x)\n      smi = '.'.join(y)\n  f = open(f'{uni}_{parameter}_input.csv', 'w')\n  f.write('name,sequence,SMILES,pdbpath\\n')\n  f.write(f'{uni},{seq},{smi},{uni}.pdb\\n')\n  f.close()\n\n  f = open(f'predict.sh', 'w')\n  f.write(f'''\nTEST_FILE_PREFIX={uni}_{parameter}\nRECORDS_FILE=${{TEST_FILE_PREFIX}}.json\nCHECKPOINT_DIR=./production_models/{parameter}/\n\npython ./scripts/create_pdbrecords.py --data_file ${{TEST_FILE_PREFIX}}_input.csv --out_file ${{RECORDS_FILE}}\npython predict.py --test_path ${{TEST_FILE_PREFIX}}_input.csv --preds_path ${{TEST_FILE_PREFIX}}_output.csv --checkpoint_dir $CHECKPOINT_DIR --uncertainty_method mve --smiles_column SMILES --individual_ensemble_predictions --protein_records_path $RECORDS_FILE\n''')\n  f.close()\n\n  print('Input success!')\n  print('Enzyme sequence length:', len(sequence))\n  print('Substrate structure:')\n  # display(ShowMols([mol]))\n\n  return seq, smi\n\nseq, smi = create_csv_sh(parameter, uniprot_id, sequence, SMILES)\n\nprint('Predicting.. This will take a while..\\n')\n\ndef get_predictions(parameter, uniprot_id):\n  df = pd.read_csv(f'{uniprot_id}_{parameter}_output.csv')\n  unit = ' mM'\n  if parameter=='kcat':\n    parameter_print = 'k_{cat}'\n    parameter_print_log = 'log_{10}(k_{cat})'\n    target_col = 'log10kcat_max'\n    unit = ' s^{-1}'\n  elif parameter=='km':\n    target_col = 'log10km_mean'\n    parameter_print = 'K_{m}'\n    parameter_print_log = 'log_{10}(K_{m})'\n  else:\n    target_col = 'log10ki_mean'\n    parameter_print = 'K_{i}'\n    parameter_print_log = 'log_{10}(K_{i})'\n\n  unc_col = f'{target_col}_mve_uncal_var'\n  model_cols = [col for col in df.columns if col.startswith(target_col) and 'model_' in col]\n\n  unc = df[unc_col].iloc[0]\n\n  prediction = df[target_col].iloc[0]\n  prediction_linear = np.power(10, prediction)\n\n  model_out = df[target_col].iloc[0]\n  model_outs = np.array([df[col].iloc[0] for col in model_cols])\n  # print(model_outs)\n  epi_unc = np.var(model_outs)#np.sum(np.power(2, model_outs))/10. - np.power(2, model_out)\n  alea_unc = unc - epi_unc\n  epi_unc = np.sqrt(epi_unc)\n  alea_unc = np.sqrt(alea_unc)\n  unc = np.sqrt(unc)\n\n  # print(unc-epi_unc-alea_unc)\n  # def display_outs(prediction_type, out, alea_output, epi_output, unit):\n  display(Math((parameter_print + f' = {prediction_linear:.5f}'+ unit)))\n  print('\\n')\n  display(Math((parameter_print_log + f' = {prediction:.5f}')))\n  display(Math(('SD_{total}'+f' = {unc:.5f}')))\n  display(Math(('SD_{aleatoric}'+f' = {alea_unc:.5f}')))\n  display(Math(('SD_{epistemic}'+f' = {epi_unc:.5f}')))\n\nif use_cpu:\n    os.system(\"export PROTEIN_EMBED_USE_CPU=1;./predict.sh >/dev/null 2>&1\")\nelse:\n    os.system(\"export PROTEIN_EMBED_USE_CPU=0;./predict.sh >/dev/null 2>&1\")\n\nget_predictions(parameter,uniprot_id)"}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.8.5"}},"nbformat":4,"nbformat_minor":5}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "single-title",
+   "metadata": {},
+   "source": "## Define inputs"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "single-params",
+   "metadata": {},
+   "outputs": [],
+   "source": "parameter = 'km'  # allowed values: ['kcat', 'km', 'ki']\nparameter = parameter.lower()\n\nuse_cpu = 1  # set to 0 if you have GPU enabled\n\nuniprot_id = 'P35557'\n# If you do not have a UniProt ID, enter another stable identifier such as 'enzyme1'.\nsequence = 'MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ'\nSMILES = 'C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O'\n"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "single-run-note",
+   "metadata": {},
+   "source": "## Run prediction"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "single-run",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": "from pathlib import Path\n\nimport pandas as pd\nfrom catpred.inference import PredictionRequest, run_prediction_pipeline\n\ninput_file = Path(f'{uniprot_id}_{parameter}.csv')\ninput_df = pd.DataFrame([\n    {\n        'name': uniprot_id,\n        'sequence': sequence,\n        'SMILES': SMILES,\n        'pdbpath': f'{uniprot_id}.pdb',\n    }\n])\ninput_df.to_csv(input_file, index=False)\n\ncheckpoint_dir = f'../data/pretrained/production/{parameter}'\nrequest = PredictionRequest(\n    parameter=parameter,\n    input_file=str(input_file),\n    checkpoint_dir=checkpoint_dir,\n    use_gpu=not bool(use_cpu),\n    repo_root='.',\n)\n\noutfile = run_prediction_pipeline(request=request, results_dir='results')\noutput_final = pd.read_csv(outfile)\nprint('Output saved to', outfile)\noutput_final\n"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}