{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import Auto3D\nfrom Auto3D import Auto3DOptions, main\nfrom Auto3D.tautomer import get_stable_tautomers"
  },
  {
   "cell_type": "markdown",
   "source": "# Tautomer Enumeration with Custom NNP\n\nThis notebook demonstrates how to use a custom neural network potential for tautomer enumeration.",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a custom NNP\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import os, sys\nroot = os.path.dirname(os.path.dirname(os.path.abspath(\"__file__\")))\nsys.path.append(os.path.dirname(__file__))\n\nimport Auto3D\nfrom Auto3D import Auto3DOptions\nfrom Auto3D.tautomer import get_stable_tautomers\n\ninput_path = os.path.join(root, \"example\", \"files\", \"sildnafil.smi\")\n\nif __name__ == \"__main__\":\n    config = Auto3DOptions(\n        path=input_path, k=1, enumerate_tautomer=True, tauto_engine=\"rdkit\",\n        optimizing_engine=\"userNNP\",  # Use userNNP for tautomers\n        max_confs=10, patience=200, use_gpu=False\n    )\n    tautomer_out = get_stable_tautomers(config, tauto_k=3)"
  }
 ],
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