DebuggingEfforts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Current Debugging Status "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Broad codebase overview: \n",
    "\n",
    "* I will focus on the functional parts of the code needed for integrating and testing SDF integration, there is other code that comes along with downloading it from the authors, such as modifying the XML table or dealing with the .db2 format, but I haven't messed with that.\n",
    "   \n",
    "* The main script is \"Torsion_Strain.py\" and it opens/imports functions from \"TL_Functions.py\". I've included them here, but I am not going to use the original files directly. I am going to use my \"refactor_\" files, they work the same in terms of the scientific output but they are easier to work with. The original code has these issues: \n",
    "  \n",
    "* * (1) a lot of comments which make them very hard to read. \n",
    "  \n",
    "* * (2) have some very undesired functionality for taking input/output files that can make it easy to overwrite your work, so I replaced that with argparse.\n",
    "\n",
    "* * (3) Have extra code related to db2 files that we don't have (like he doesn't provide you with any) and also make it hard to read, so that is gone too. \n",
    "  \n",
    "* * (4) A lot of \"getter and setter\" methods that seem to be considered poor practice in Python and are also arguably more confusing than just not having them, so I removed them. Getters and setters are often used for managing global/private states in Java (as far as ChatGPT has told me) or for debugging in Python, but they aren't useful to the debugging I've had to do. \n",
    "\n",
    "* * (5) Changed his open(exec()) statement in Torsion_Strain.py to an import call. This is the same functionality except I can much more easily follow the code and make less mistakes when calling files. I think the original programmer came from Java and does a lot of stuff I generally have a hard time following with my background in python.\n",
    "\n",
    "* My refactor_ versions (and the refactor_X_debug versions) do have some changes that do different functionality, however that is only related to adding a function to process SDF files (which we need) and debugging elements that do not change the underlying functionality of the code. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What does the code do? \n",
    "\n",
    "It's really hard to describe well in natural language exactly what the code is doing. I've re-read it and the paper a lot and I mostly get it now. The logical flow of the code is more or less this:\n",
    "\n",
    "Torsion_Strain.py takes an input files (mol2/dbs, my sdf, etc) and calls TL_functions.py to act on it, culminating in writing out a CSV that contains these fields: \n",
    "\n",
    "Molecule_ID, Total_Energy, Energy_Lower_Bound, Energy_Upper_Bound\tNum_Torsion_Patterns, Torsion_1_Energy, Torsion_1_Energy_Lower_Bound, Torsion_1_Energy_Upper_Bound, Torsion_1_Atom_Indices, Torsion_1_Dihedral_Angle, Torsion_1_SMARTS_Pattern, Torsion_1_Hierarchy_Class, Torsion_1_Method, Torsion_1_Indices_Reversed, Torsion_1_Rule_Index, Torsion_2_Energy...\n",
    "\n",
    "And then it would loop all those Torsion fields depending on how many torsions are found in the molecule. \"Total_Energy\" is the sum of all the Torsion energies found for a gives Molecule_ID. \n",
    "\n",
    "The values of all these fields come from the methods in TL_functions.py. This get's really hard to explain, but essentially TL_lookup is called on an rdkit mol object we get from reading our mol2/sdf file, and then it does all the logic required to find all the smarts patterns that can be linked to a XML database of torsion strains, and then all the logic of how the author thinks the various parameters therein should be processed. This is easiest to qualitatively understand from reading the paper, as I have to be honest, I am still very confused at that specific part of the code that is deciding *which* Torsion pattern should be matched based on all this stuff about the hierarchy class/method/etc. \n",
    "\n",
    "However, what I can tell you is that basic logic that defines what we search in this XML database.\n",
    "\n",
    "The mol2s/sdf function return a dictionary of {Molecule_ID: rdkit_mol_object} and then the rest of the code uses the mol object. Since the XML library is essentially just a library of SMARTs patterns and their associated torsion energies, he matches them like this in ```def tp_match```: ``` matches = mol.GetSubstructMatches(Chem.MolFromSmarts(smarts))```\n",
    "\n",
    "This returns a list of tuples corresponding the atomic indices that match this smarts pattern (this will make more sense later). I have a demo of this visual examples. It is important to note that this is where most of my issues seem to be rooted in. Something strange is happening here (or later) because as far as I can tell: ```GetSubstructMatches``` (an rdkit function) is going to find/return the tuples based on what your atom indexes are. *So it should not matter how one imports an identical molecules (mol2/sdf) as long as their indexes match*. Mine *do match* and yet for some reason I can't understand, we get different tuples. \n",
    "\n",
    "This is almost a seperate issue from the actual torsion energies calculation. I thought maybe my mol2 -> sdf conversion messed up some 3D data that is used for the calculation. But even if that's true, two equivalent 2D molecules should have equivalent ```GetSubstructMatches```, yet I am getting some weird behavior. \n",
    "\n",
    "In situations where this issue does not appear, *I get identical torsion energies to the authors*, indicating that it is not the 3D aspect of the conversion. \n",
    "\n",
    "Thus, the issue, to me is somewhere in what actually happens to ```GetSubstructMatches``` related data as he processes it, but that part of the code is still something I need to figure out. \n",
    "\n",
    "Now, I'll share the debugging and visualizations I have done, and then get back to figuring out what I haven't solved yet. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion\n",
    "\n",
    "Conversion of mol2 to sdf was handled in OpenBabel like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from openbabel import pybel\n",
    "# def convert_mol2_to_sdf(mol2_file, sdf_file):\n",
    "#    mol2_mols = pybel.readfile(\"mol2\", mol2_file)\n",
    "#    out_sdf = pybel.Outputfile(\"sdf\", sdf_file, overwrite=True)\n",
    "\n",
    "#    for mol in mol2_mols:\n",
    "#        out_sdf.write(mol)\n",
    "\n",
    "#    out_sdf.close()\n",
    "\n",
    "# Example usage\n",
    "# convert_mol2_to_sdf('/Users/lkv206/Downloads/STRAIN_FILTER/example.mol2', 'output.sdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Draw, rdFMCS\n",
    "from openbabel import pybel\n",
    "import pandas as pd\n",
    "import re\n",
    "from IPython.display import display\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Supplier functions to make the required rdkit mol objects\n",
    "from refactor_TL_Functions import Mol2MolSupplier, custom_sdmolsupplier_H\n",
    "\n",
    "# Create list of names and dictionary of {names: mol objects} using Mol2MolSupplier\n",
    "mol2_names, mol2_mols = Mol2MolSupplier('example.mol2')\n",
    "\n",
    "# Create list of names and dictionary of {names: mol objects} using custom_sdmolsupplier_H\n",
    "sdf_names, sdf_mols = custom_sdmolsupplier_H('output.sdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZINC000191583186', 'ZINC000159533726', 'ZINC000151228439']\n",
      "['ZINC000191583186', 'ZINC000159533726', 'ZINC000151228439']\n"
     ]
    }
   ],
   "source": [
    "print(mol2_names)\n",
    "print(sdf_names) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, mol2: ZINC000191583186\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, sdf: ZINC000191583186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, mol2: ZINC000159533726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, sdf: ZINC000159533726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, mol2: ZINC000151228439\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, sdf: ZINC000151228439\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since the molecule names (the keys of the dict) are identical, we can visualizate both sets of molecules in 3D to visually compare them in 3D. This is a qualitative check of how rdkit is \"seeing\" the molecules.\n",
    "\n",
    "# Get the keys from one of the dictionaries\n",
    "keys = list(mol2_mols.keys())\n",
    "\n",
    "# Iterate over the keys\n",
    "for key in keys:\n",
    "    # Get the molecule from each dictionary\n",
    "    mol_mol2 = mol2_mols[key]\n",
    "    mol_sdf = sdf_mols[key]\n",
    "\n",
    "    # Draw the mol2 molecule with atom indices\n",
    "    img_mol2 = Draw.MolToImage(mol_mol2, size=(300, 300))\n",
    "\n",
    "    # Display the key and the image\n",
    "    print(f\"Key, mol2: {key}\")\n",
    "    display(img_mol2)\n",
    "\n",
    "    # Draw the sdf molecule with atom indices\n",
    "    img_sdf = Draw.MolToImage(mol_sdf, size=(300, 300))\n",
    "\n",
    "    # Display the key and the image\n",
    "    print(f\"Key, sdf: {key}\")\n",
    "    display(img_sdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, mol2_H: ZINC000191583186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x28ff66180>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, sdf_H: ZINC000191583186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x28ea78900>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, mol2_H: ZINC000159533726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x28ff661f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, sdf_H: ZINC000159533726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x28ea78890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, mol2_H: ZINC000151228439\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x28ff65930>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key, sdf_H: ZINC000151228439\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x28ff65700>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "IPythonConsole.molSize = 600, 600\n",
    "IPythonConsole.drawOptions.addAtomIndices = True\n",
    "\n",
    "# Get the keys from one of the dictionaries\n",
    "keys = list(mol2_mols.keys())\n",
    "\n",
    "# Iterate over the keys\n",
    "for key in keys:\n",
    "    # Get the molecule from each dictionary\n",
    "    mol_mol2_2D = mol2_mols[key]\n",
    "    mol_sdf_2D = sdf_mols[key]\n",
    "\n",
    "    # Compute 2D coordinates for the mol2 molecule\n",
    "    AllChem.Compute2DCoords(mol_mol2_2D)\n",
    "\n",
    "    # Display the key and the mol2 molecule\n",
    "    print(f\"Key, mol2_2D: {key}\")\n",
    "    display(mol_mol2_2D)\n",
    "\n",
    "    # Compute 2D coordinates for the sdf molecule\n",
    "    AllChem.Compute2DCoords(mol_sdf_2D)\n",
    "\n",
    "    # Display the key and the sdf molecule\n",
    "    print(f\"Key, sdf_2D: {key}\")\n",
    "    display(mol_sdf_2D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, the only difference I can notice is some strange chirality settings coming from the default Mol2 supplier function the author used. However, the chirality os somewhat non-sensical because it ascribes putative stereocenters two molecules that share two identical substitutents (e.g. not chiral). Maybe something weird here is causing the mismatch, but I haven't found a way to make my sdf supplier function work like that. \n",
    "\n",
    "What I can see however, is that the molecules appear to have a relatively identical 3D shape and identical atom indicies. Rigorously setting equivalent 3D positions seems tricky (as opposed to relative), but I think I have other evidence later that this doesn't matter. Translation/rotation does not affect angles between atoms/bonds in a intramolecular fashion, only intermolecular - which does not matter in torsion. \n",
    "\n",
    "However, I was worried that the graphical atom indices used here aren't actually real. Based on rdkit, documentation, we can check the true atomic indicies like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecule: ZINC000191583186\n",
      "Molecules match!\n",
      "Custom molecule atom data:\n"
     ]
    },
    {
     "data": {
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       "        0  1  2  3  4  5  6  7  8  9   10  11  12  13  14  15  16  17  18  19  \\\n",
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      "Mol2 molecule atom data:\n"
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      "Molecule: ZINC000159533726\n",
      "Molecules match!\n",
      "Custom molecule atom data:\n"
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      "Molecule: ZINC000151228439\n",
      "Molecules match!\n",
      "Custom molecule atom data:\n"
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       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>N</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1  2  3  4  5  6  7  8  9   10  11  12  13  14  15  16  17  18  19  \\\n",
       "AtomIdx  0  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19   \n",
       "Symbol   C  C  H  N  H  H  C  C  C  O   C   C   C   C   C   C   C   C   N   C   \n",
       "\n",
       "         20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  \\\n",
       "AtomIdx  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36   \n",
       "Symbol    C   C   C   C   C   N   N   H   H   H   H   H   H   H   H   H   H   \n",
       "\n",
       "         37  38  39  40  41  42  43  44  45  46  \n",
       "AtomIdx  37  38  39  40  41  42  43  44  45  46  \n",
       "Symbol    H   H   H   H   H   H   H   H   H   H  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prevent truncating of dataframe display\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "\n",
    "# Load the molecules with the custom SDF function\n",
    "names_custom, mols_custom = custom_sdmolsupplier_H(\"output.sdf\")\n",
    "\n",
    "# Load the molecules with Mol2MolSupplier\n",
    "names_mol2, mols_mol2 = Mol2MolSupplier(\"example.mol2\")\n",
    "\n",
    "\n",
    "# Define a method to compare the atom indicies of two molecules so we can loop over our three molecules. If the molecules match, we return True and the atom data as dataframes. If the molecules do not match, we return False and None for the dataframes.\n",
    "def compare_molecules(mol1, mol2):\n",
    "    atoms1 = [(atom.GetIdx(), atom.GetSymbol()) for atom in mol1.GetAtoms()]\n",
    "    atoms2 = [(atom.GetIdx(), atom.GetSymbol()) for atom in mol2.GetAtoms()]\n",
    "\n",
    "    if atoms1 != atoms2:\n",
    "        print(f\"Discrepancy found: {atoms1} vs {atoms2}\")\n",
    "        return False, None, None\n",
    "\n",
    "    # Convert the atom data to dataframes\n",
    "    df1 = pd.DataFrame(atoms1, columns=[\"AtomIdx\", \"Symbol\"])\n",
    "    df2 = pd.DataFrame(atoms2, columns=[\"AtomIdx\", \"Symbol\"])\n",
    "\n",
    "    return True, df1, df2\n",
    "\n",
    "\n",
    "# Iterate over all molecules\n",
    "for name in names_custom:\n",
    "    mol_custom = mols_custom[name]\n",
    "    mol_mol2 = mols_mol2[name]\n",
    "\n",
    "    print(f\"Molecule: {name}\")\n",
    "    match, df_custom, df_mol2 = compare_molecules(mol_custom, mol_mol2)\n",
    "    if not match:\n",
    "        print(\"Molecules do not match!\")\n",
    "    else:\n",
    "        print(\"Molecules match!\")\n",
    "        print(\"Custom molecule atom data:\")\n",
    "        display(df_custom.T)\n",
    "        print(\"Mol2 molecule atom data:\")\n",
    "        display(df_mol2.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, despite what appears like 2D and 3D equivalence (except for the weird chirality thing), I can show you that the results are unfortunately different: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 molecules finished reading. Calculating strain energy...\n",
      "3 successful / 0 NA. Please check: mol2.csv\n"
     ]
    }
   ],
   "source": [
    "%run refactor_Torsion_Strain.py -i example.mol2 -o mol2.csv \n",
    "#NOTE: I do not allow the user to overwrite file names, this won't run unless you change \"mol2.csv\"\n",
    "#I use mol2.csv in the analysis so if you want to test my code, just pick a different name "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 molecules finished reading. Calculating strain energy...\n",
      "3 successful / 0 NA. Please check: sdf.csv\n"
     ]
    }
   ],
   "source": [
    "%run refactor_Torsion_Strain.py -i output.sdf -o sdf.csv \n",
    "#NOTE: I do not allow the user to overwrite file names, this won't run unless you change \"sdf.csv\"\n",
    "#I use sdf.csv in the analysis so if you want to test my code, just pick a different name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_dataframes(file1, file2):\n",
    "    # Define a regular expression for the separator\n",
    "    sep = r',(?![^[]*\\])'  # Pass the compiled regular expression pattern as a string\n",
    "\n",
    "    # Determine the origin from the filename\n",
    "    origin1 = 'sdf' if 'sdf' in file1 else 'mol2'\n",
    "    origin2 = 'sdf' if 'sdf' in file2 else 'mol2'\n",
    "\n",
    "    dataframes = []\n",
    "\n",
    "    with open(file1, \"r\") as f1, open(file2, \"r\") as f2:\n",
    "        reader1 = csv.reader(f1)\n",
    "        reader2 = csv.reader(f2)\n",
    "\n",
    "        for line1, line2 in zip(reader1, reader2):\n",
    "            # Define general column names\n",
    "            column_names = [\"Molecule_ID\", \"Total_Energy\", \"Energy_Lower_Bound\", \"Energy_Upper_Bound\", \"Num_Torsion_Patterns\"]\n",
    "\n",
    "            # Calculate the number of torsion patterns\n",
    "            num_torsion_patterns = (len(line1) - len(column_names)) // 10\n",
    "\n",
    "            # Generate column names for each torsion pattern\n",
    "            for i in range(1, num_torsion_patterns + 1):\n",
    "                column_names.extend([\n",
    "                    f\"Torsion_{i}_Energy\", f\"Torsion_{i}_Energy_Lower_Bound\", \n",
    "                    f\"Torsion_{i}_Energy_Upper_Bound\", f\"Torsion_{i}_Atom_Indices\", \n",
    "                    f\"Torsion_{i}_Dihedral_Angle\", f\"Torsion_{i}_SMARTS_Pattern\", \n",
    "                    f\"Torsion_{i}_Hierarchy_Class\", f\"Torsion_{i}_Method\", \n",
    "                    f\"Torsion_{i}_Indices_Reversed\", f\"Torsion_{i}_Rule_Index\"\n",
    "                ])\n",
    "\n",
    "            # Slice the lines based on the number of torsion patterns\n",
    "            line1 = line1[:len(column_names)]\n",
    "            line2 = line2[:len(column_names)]\n",
    "\n",
    "            # Convert lines to DataFrames\n",
    "            df1 = pd.DataFrame([line1], columns=column_names)\n",
    "            df2 = pd.DataFrame([line2], columns=column_names)\n",
    "\n",
    "            # Add the origin to the DataFrames\n",
    "            df1.insert(0, 'Origin', origin1)\n",
    "            df2.insert(0, 'Origin', origin2)\n",
    "\n",
    "            # Concatenate the new DataFrame with df and drop the index\n",
    "            df = pd.concat([df1, df2], ignore_index=True)\n",
    "\n",
    "            dataframes.append(df)\n",
    "\n",
    "            # Reset column names for next line\n",
    "            column_names = column_names[:5]\n",
    "\n",
    "    return dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Molecule_ID</th>\n",
       "      <th>Total_Energy</th>\n",
       "      <th>Energy_Lower_Bound</th>\n",
       "      <th>Energy_Upper_Bound</th>\n",
       "      <th>Num_Torsion_Patterns</th>\n",
       "      <th>Torsion_1_Energy</th>\n",
       "      <th>Torsion_1_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_1_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_1_Atom_Indices</th>\n",
       "      <th>Torsion_1_Dihedral_Angle</th>\n",
       "      <th>Torsion_1_SMARTS_Pattern</th>\n",
       "      <th>Torsion_1_Hierarchy_Class</th>\n",
       "      <th>Torsion_1_Method</th>\n",
       "      <th>Torsion_1_Indices_Reversed</th>\n",
       "      <th>Torsion_1_Rule_Index</th>\n",
       "      <th>Torsion_2_Energy</th>\n",
       "      <th>Torsion_2_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_2_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_2_Atom_Indices</th>\n",
       "      <th>Torsion_2_Dihedral_Angle</th>\n",
       "      <th>Torsion_2_SMARTS_Pattern</th>\n",
       "      <th>Torsion_2_Hierarchy_Class</th>\n",
       "      <th>Torsion_2_Method</th>\n",
       "      <th>Torsion_2_Indices_Reversed</th>\n",
       "      <th>Torsion_2_Rule_Index</th>\n",
       "      <th>Torsion_3_Energy</th>\n",
       "      <th>Torsion_3_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_3_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_3_Atom_Indices</th>\n",
       "      <th>Torsion_3_Dihedral_Angle</th>\n",
       "      <th>Torsion_3_SMARTS_Pattern</th>\n",
       "      <th>Torsion_3_Hierarchy_Class</th>\n",
       "      <th>Torsion_3_Method</th>\n",
       "      <th>Torsion_3_Indices_Reversed</th>\n",
       "      <th>Torsion_3_Rule_Index</th>\n",
       "      <th>Torsion_4_Energy</th>\n",
       "      <th>Torsion_4_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_4_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_4_Atom_Indices</th>\n",
       "      <th>Torsion_4_Dihedral_Angle</th>\n",
       "      <th>Torsion_4_SMARTS_Pattern</th>\n",
       "      <th>Torsion_4_Hierarchy_Class</th>\n",
       "      <th>Torsion_4_Method</th>\n",
       "      <th>Torsion_4_Indices_Reversed</th>\n",
       "      <th>Torsion_4_Rule_Index</th>\n",
       "      <th>Torsion_5_Energy</th>\n",
       "      <th>Torsion_5_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_5_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_5_Atom_Indices</th>\n",
       "      <th>Torsion_5_Dihedral_Angle</th>\n",
       "      <th>Torsion_5_SMARTS_Pattern</th>\n",
       "      <th>Torsion_5_Hierarchy_Class</th>\n",
       "      <th>Torsion_5_Method</th>\n",
       "      <th>Torsion_5_Indices_Reversed</th>\n",
       "      <th>Torsion_5_Rule_Index</th>\n",
       "      <th>Torsion_6_Energy</th>\n",
       "      <th>Torsion_6_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_6_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_6_Atom_Indices</th>\n",
       "      <th>Torsion_6_Dihedral_Angle</th>\n",
       "      <th>Torsion_6_SMARTS_Pattern</th>\n",
       "      <th>Torsion_6_Hierarchy_Class</th>\n",
       "      <th>Torsion_6_Method</th>\n",
       "      <th>Torsion_6_Indices_Reversed</th>\n",
       "      <th>Torsion_6_Rule_Index</th>\n",
       "      <th>Torsion_7_Energy</th>\n",
       "      <th>Torsion_7_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_7_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_7_Atom_Indices</th>\n",
       "      <th>Torsion_7_Dihedral_Angle</th>\n",
       "      <th>Torsion_7_SMARTS_Pattern</th>\n",
       "      <th>Torsion_7_Hierarchy_Class</th>\n",
       "      <th>Torsion_7_Method</th>\n",
       "      <th>Torsion_7_Indices_Reversed</th>\n",
       "      <th>Torsion_7_Rule_Index</th>\n",
       "      <th>Torsion_8_Energy</th>\n",
       "      <th>Torsion_8_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_8_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_8_Atom_Indices</th>\n",
       "      <th>Torsion_8_Dihedral_Angle</th>\n",
       "      <th>Torsion_8_SMARTS_Pattern</th>\n",
       "      <th>Torsion_8_Hierarchy_Class</th>\n",
       "      <th>Torsion_8_Method</th>\n",
       "      <th>Torsion_8_Indices_Reversed</th>\n",
       "      <th>Torsion_8_Rule_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sdf</td>\n",
       "      <td>ZINC000191583186</td>\n",
       "      <td>5.605219996833657</td>\n",
       "      <td>5.056429357277703</td>\n",
       "      <td>nan</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5149015019717655</td>\n",
       "      <td>1.4720415536611848</td>\n",
       "      <td>1.5610762283636115</td>\n",
       "      <td>[15, 13, 16, 17]</td>\n",
       "      <td>-79.9987689699916</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>1.1629499135175863</td>\n",
       "      <td>1.0138763654377367</td>\n",
       "      <td>nan</td>\n",
       "      <td>[16, 13, 10, 6]</td>\n",
       "      <td>-159.99678602096148</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>1.1627673850560352</td>\n",
       "      <td>1.0137656230416119</td>\n",
       "      <td>nan</td>\n",
       "      <td>[8, 6, 10, 13]</td>\n",
       "      <td>-159.9987199068972</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0408019376654853</td>\n",
       "      <td>0.8903165100240227</td>\n",
       "      <td>1.2452601666364487</td>\n",
       "      <td>[19, 18, 17, 16]</td>\n",
       "      <td>0.27343903595492747</td>\n",
       "      <td>[a:1][c:2]!@[CX4H2:3][CX4:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.34528595288770225</td>\n",
       "      <td>0.33914296320237924</td>\n",
       "      <td>0.3514895675876632</td>\n",
       "      <td>[4, 5, 6, 8]</td>\n",
       "      <td>59.99348627709831</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34522247008497436</td>\n",
       "      <td>0.33908125045590787</td>\n",
       "      <td>0.3514242249136928</td>\n",
       "      <td>[5, 6, 8, 9]</td>\n",
       "      <td>60.00831008335173</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02635434808368613</td>\n",
       "      <td>0.013789212541832876</td>\n",
       "      <td>0.03918709847466442</td>\n",
       "      <td>[18, 23, 24, 25]</td>\n",
       "      <td>-0.0040091720895600565</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>0.006707819673103034</td>\n",
       "      <td>-0.022299325452382023</td>\n",
       "      <td>0.037155275308878924</td>\n",
       "      <td>[27, 4, 5, 6]</td>\n",
       "      <td>89.99270199528776</td>\n",
       "      <td>[a:1][c:2]!@[CX4H2:3][CX4:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mol2</td>\n",
       "      <td>ZINC000191583186</td>\n",
       "      <td>5.621730380254799</td>\n",
       "      <td>5.0724614891320625</td>\n",
       "      <td>nan</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5149015019717655</td>\n",
       "      <td>1.4720415536611848</td>\n",
       "      <td>1.5610762283636115</td>\n",
       "      <td>[15, 13, 16, 17]</td>\n",
       "      <td>-79.9987689699916</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>1.1629499135175863</td>\n",
       "      <td>1.0138763654377367</td>\n",
       "      <td>nan</td>\n",
       "      <td>[16, 13, 10, 6]</td>\n",
       "      <td>-159.99678602096148</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>1.1627673850560352</td>\n",
       "      <td>1.0137656230416119</td>\n",
       "      <td>nan</td>\n",
       "      <td>[8, 6, 10, 13]</td>\n",
       "      <td>-159.9987199068972</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0408019376654853</td>\n",
       "      <td>0.8903165100240227</td>\n",
       "      <td>1.2452601666364487</td>\n",
       "      <td>[19, 18, 17, 16]</td>\n",
       "      <td>0.27343903595492747</td>\n",
       "      <td>[a:1][c:2]!@[CX4H2:3][CX4:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.34528595288770225</td>\n",
       "      <td>0.33914296320237924</td>\n",
       "      <td>0.3514895675876632</td>\n",
       "      <td>[4, 5, 6, 8]</td>\n",
       "      <td>59.99348627709831</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34522247008497436</td>\n",
       "      <td>0.33908125045590787</td>\n",
       "      <td>0.3514242249136928</td>\n",
       "      <td>[5, 6, 8, 9]</td>\n",
       "      <td>60.00831008335173</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.04286473150482756</td>\n",
       "      <td>0.029821344396192107</td>\n",
       "      <td>0.056205983956472805</td>\n",
       "      <td>[22, 23, 24, 26]</td>\n",
       "      <td>0.44585819818713934</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>0.006707819673103034</td>\n",
       "      <td>-0.022299325452382023</td>\n",
       "      <td>0.037155275308878924</td>\n",
       "      <td>[27, 4, 5, 6]</td>\n",
       "      <td>89.99270199528776</td>\n",
       "      <td>[a:1][c:2]!@[CX4H2:3][CX4:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Origin       Molecule_ID       Total_Energy  Energy_Lower_Bound  \\\n",
       "0    sdf  ZINC000191583186  5.605219996833657   5.056429357277703   \n",
       "1   mol2  ZINC000191583186  5.621730380254799  5.0724614891320625   \n",
       "\n",
       "  Energy_Upper_Bound Num_Torsion_Patterns    Torsion_1_Energy  \\\n",
       "0                nan                    4  1.5149015019717655   \n",
       "1                nan                    4  1.5149015019717655   \n",
       "\n",
       "  Torsion_1_Energy_Lower_Bound Torsion_1_Energy_Upper_Bound  \\\n",
       "0           1.4720415536611848           1.5610762283636115   \n",
       "1           1.4720415536611848           1.5610762283636115   \n",
       "\n",
       "  Torsion_1_Atom_Indices Torsion_1_Dihedral_Angle  \\\n",
       "0       [15, 13, 16, 17]        -79.9987689699916   \n",
       "1       [15, 13, 16, 17]        -79.9987689699916   \n",
       "\n",
       "         Torsion_1_SMARTS_Pattern Torsion_1_Hierarchy_Class Torsion_1_Method  \\\n",
       "0  [!#1:1][CX4:2]!@[CX4:3][!#1:4]                  specific            exact   \n",
       "1  [!#1:1][CX4:2]!@[CX4:3][!#1:4]                  specific            exact   \n",
       "\n",
       "  Torsion_1_Indices_Reversed Torsion_1_Rule_Index    Torsion_2_Energy  \\\n",
       "0                      False                    8  1.1629499135175863   \n",
       "1                      False                    8  1.1629499135175863   \n",
       "\n",
       "  Torsion_2_Energy_Lower_Bound Torsion_2_Energy_Upper_Bound  \\\n",
       "0           1.0138763654377367                          nan   \n",
       "1           1.0138763654377367                          nan   \n",
       "\n",
       "  Torsion_2_Atom_Indices Torsion_2_Dihedral_Angle  \\\n",
       "0        [16, 13, 10, 6]      -159.99678602096148   \n",
       "1        [16, 13, 10, 6]      -159.99678602096148   \n",
       "\n",
       "       Torsion_2_SMARTS_Pattern Torsion_2_Hierarchy_Class Torsion_2_Method  \\\n",
       "0  [*:1]~[CX4:2]!@[NX4:3]~[*:4]                   general            exact   \n",
       "1  [*:1]~[CX4:2]!@[NX4:3]~[*:4]                   general            exact   \n",
       "\n",
       "  Torsion_2_Indices_Reversed Torsion_2_Rule_Index    Torsion_3_Energy  \\\n",
       "0                      False                    7  1.1627673850560352   \n",
       "1                      False                    7  1.1627673850560352   \n",
       "\n",
       "  Torsion_3_Energy_Lower_Bound Torsion_3_Energy_Upper_Bound  \\\n",
       "0           1.0137656230416119                          nan   \n",
       "1           1.0137656230416119                          nan   \n",
       "\n",
       "  Torsion_3_Atom_Indices Torsion_3_Dihedral_Angle  \\\n",
       "0         [8, 6, 10, 13]       -159.9987199068972   \n",
       "1         [8, 6, 10, 13]       -159.9987199068972   \n",
       "\n",
       "       Torsion_3_SMARTS_Pattern Torsion_3_Hierarchy_Class Torsion_3_Method  \\\n",
       "0  [*:1]~[CX4:2]!@[NX4:3]~[*:4]                   general            exact   \n",
       "1  [*:1]~[CX4:2]!@[NX4:3]~[*:4]                   general            exact   \n",
       "\n",
       "  Torsion_3_Indices_Reversed Torsion_3_Rule_Index    Torsion_4_Energy  \\\n",
       "0                      False                    5  1.0408019376654853   \n",
       "1                      False                    5  1.0408019376654853   \n",
       "\n",
       "  Torsion_4_Energy_Lower_Bound Torsion_4_Energy_Upper_Bound  \\\n",
       "0           0.8903165100240227           1.2452601666364487   \n",
       "1           0.8903165100240227           1.2452601666364487   \n",
       "\n",
       "  Torsion_4_Atom_Indices Torsion_4_Dihedral_Angle  \\\n",
       "0       [19, 18, 17, 16]      0.27343903595492747   \n",
       "1       [19, 18, 17, 16]      0.27343903595492747   \n",
       "\n",
       "       Torsion_4_SMARTS_Pattern Torsion_4_Hierarchy_Class Torsion_4_Method  \\\n",
       "0  [a:1][c:2]!@[CX4H2:3][CX4:4]                  specific            exact   \n",
       "1  [a:1][c:2]!@[CX4H2:3][CX4:4]                  specific            exact   \n",
       "\n",
       "  Torsion_4_Indices_Reversed Torsion_4_Rule_Index     Torsion_5_Energy  \\\n",
       "0                      False                    3  0.34528595288770225   \n",
       "1                      False                    3  0.34528595288770225   \n",
       "\n",
       "  Torsion_5_Energy_Lower_Bound Torsion_5_Energy_Upper_Bound  \\\n",
       "0          0.33914296320237924           0.3514895675876632   \n",
       "1          0.33914296320237924           0.3514895675876632   \n",
       "\n",
       "  Torsion_5_Atom_Indices Torsion_5_Dihedral_Angle  \\\n",
       "0           [4, 5, 6, 8]        59.99348627709831   \n",
       "1           [4, 5, 6, 8]        59.99348627709831   \n",
       "\n",
       "         Torsion_5_SMARTS_Pattern Torsion_5_Hierarchy_Class Torsion_5_Method  \\\n",
       "0  [!#1:1][CX4:2]!@[CX4:3][!#1:4]                  specific            exact   \n",
       "1  [!#1:1][CX4:2]!@[CX4:3][!#1:4]                  specific            exact   \n",
       "\n",
       "  Torsion_5_Indices_Reversed Torsion_5_Rule_Index     Torsion_6_Energy  \\\n",
       "0                      False                    1  0.34522247008497436   \n",
       "1                      False                    1  0.34522247008497436   \n",
       "\n",
       "  Torsion_6_Energy_Lower_Bound Torsion_6_Energy_Upper_Bound  \\\n",
       "0          0.33908125045590787           0.3514242249136928   \n",
       "1          0.33908125045590787           0.3514242249136928   \n",
       "\n",
       "  Torsion_6_Atom_Indices Torsion_6_Dihedral_Angle  \\\n",
       "0           [5, 6, 8, 9]        60.00831008335173   \n",
       "1           [5, 6, 8, 9]        60.00831008335173   \n",
       "\n",
       "         Torsion_6_SMARTS_Pattern Torsion_6_Hierarchy_Class Torsion_6_Method  \\\n",
       "0  [!#1:1][CX4:2]!@[CX4:3][!#1:4]                  specific            exact   \n",
       "1  [!#1:1][CX4:2]!@[CX4:3][!#1:4]                  specific            exact   \n",
       "\n",
       "  Torsion_6_Indices_Reversed Torsion_6_Rule_Index     Torsion_7_Energy  \\\n",
       "0                      False                    0  0.02635434808368613   \n",
       "1                      False                    0  0.04286473150482756   \n",
       "\n",
       "  Torsion_7_Energy_Lower_Bound Torsion_7_Energy_Upper_Bound  \\\n",
       "0         0.013789212541832876          0.03918709847466442   \n",
       "1         0.029821344396192107         0.056205983956472805   \n",
       "\n",
       "  Torsion_7_Atom_Indices Torsion_7_Dihedral_Angle    Torsion_7_SMARTS_Pattern  \\\n",
       "0       [18, 23, 24, 25]   -0.0040091720895600565  [a:1][a:2]!@[NX3:3][!#1:4]   \n",
       "1       [22, 23, 24, 26]      0.44585819818713934  [a:1][a:2]!@[NX3:3][!#1:4]   \n",
       "\n",
       "  Torsion_7_Hierarchy_Class Torsion_7_Method Torsion_7_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_7_Rule_Index      Torsion_8_Energy Torsion_8_Energy_Lower_Bound  \\\n",
       "0                    6  0.006707819673103034        -0.022299325452382023   \n",
       "1                    6  0.006707819673103034        -0.022299325452382023   \n",
       "\n",
       "  Torsion_8_Energy_Upper_Bound Torsion_8_Atom_Indices  \\\n",
       "0         0.037155275308878924          [27, 4, 5, 6]   \n",
       "1         0.037155275308878924          [27, 4, 5, 6]   \n",
       "\n",
       "  Torsion_8_Dihedral_Angle      Torsion_8_SMARTS_Pattern  \\\n",
       "0        89.99270199528776  [a:1][c:2]!@[CX4H2:3][CX4:4]   \n",
       "1        89.99270199528776  [a:1][c:2]!@[CX4H2:3][CX4:4]   \n",
       "\n",
       "  Torsion_8_Hierarchy_Class Torsion_8_Method Torsion_8_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_8_Rule_Index  \n",
       "0                    2  \n",
       "1                    2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Molecule_ID</th>\n",
       "      <th>Total_Energy</th>\n",
       "      <th>Energy_Lower_Bound</th>\n",
       "      <th>Energy_Upper_Bound</th>\n",
       "      <th>Num_Torsion_Patterns</th>\n",
       "      <th>Torsion_1_Energy</th>\n",
       "      <th>Torsion_1_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_1_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_1_Atom_Indices</th>\n",
       "      <th>Torsion_1_Dihedral_Angle</th>\n",
       "      <th>Torsion_1_SMARTS_Pattern</th>\n",
       "      <th>Torsion_1_Hierarchy_Class</th>\n",
       "      <th>Torsion_1_Method</th>\n",
       "      <th>Torsion_1_Indices_Reversed</th>\n",
       "      <th>Torsion_1_Rule_Index</th>\n",
       "      <th>Torsion_2_Energy</th>\n",
       "      <th>Torsion_2_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_2_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_2_Atom_Indices</th>\n",
       "      <th>Torsion_2_Dihedral_Angle</th>\n",
       "      <th>Torsion_2_SMARTS_Pattern</th>\n",
       "      <th>Torsion_2_Hierarchy_Class</th>\n",
       "      <th>Torsion_2_Method</th>\n",
       "      <th>Torsion_2_Indices_Reversed</th>\n",
       "      <th>Torsion_2_Rule_Index</th>\n",
       "      <th>Torsion_3_Energy</th>\n",
       "      <th>Torsion_3_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_3_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_3_Atom_Indices</th>\n",
       "      <th>Torsion_3_Dihedral_Angle</th>\n",
       "      <th>Torsion_3_SMARTS_Pattern</th>\n",
       "      <th>Torsion_3_Hierarchy_Class</th>\n",
       "      <th>Torsion_3_Method</th>\n",
       "      <th>Torsion_3_Indices_Reversed</th>\n",
       "      <th>Torsion_3_Rule_Index</th>\n",
       "      <th>Torsion_4_Energy</th>\n",
       "      <th>Torsion_4_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_4_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_4_Atom_Indices</th>\n",
       "      <th>Torsion_4_Dihedral_Angle</th>\n",
       "      <th>Torsion_4_SMARTS_Pattern</th>\n",
       "      <th>Torsion_4_Hierarchy_Class</th>\n",
       "      <th>Torsion_4_Method</th>\n",
       "      <th>Torsion_4_Indices_Reversed</th>\n",
       "      <th>Torsion_4_Rule_Index</th>\n",
       "      <th>Torsion_5_Energy</th>\n",
       "      <th>Torsion_5_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_5_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_5_Atom_Indices</th>\n",
       "      <th>Torsion_5_Dihedral_Angle</th>\n",
       "      <th>Torsion_5_SMARTS_Pattern</th>\n",
       "      <th>Torsion_5_Hierarchy_Class</th>\n",
       "      <th>Torsion_5_Method</th>\n",
       "      <th>Torsion_5_Indices_Reversed</th>\n",
       "      <th>Torsion_5_Rule_Index</th>\n",
       "      <th>Torsion_6_Energy</th>\n",
       "      <th>Torsion_6_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_6_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_6_Atom_Indices</th>\n",
       "      <th>Torsion_6_Dihedral_Angle</th>\n",
       "      <th>Torsion_6_SMARTS_Pattern</th>\n",
       "      <th>Torsion_6_Hierarchy_Class</th>\n",
       "      <th>Torsion_6_Method</th>\n",
       "      <th>Torsion_6_Indices_Reversed</th>\n",
       "      <th>Torsion_6_Rule_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sdf</td>\n",
       "      <td>ZINC000159533726</td>\n",
       "      <td>3.440838280823955</td>\n",
       "      <td>2.4289098751947322</td>\n",
       "      <td>nan</td>\n",
       "      <td>4</td>\n",
       "      <td>2.1206363671576907</td>\n",
       "      <td>1.2832134972295552</td>\n",
       "      <td>nan</td>\n",
       "      <td>[16, 1, 3, 4]</td>\n",
       "      <td>-120.00615479360211</td>\n",
       "      <td>[NH1:1][CX4:2]!@[CX3:3]=[O:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8752468812310884</td>\n",
       "      <td>0.773987448810736</td>\n",
       "      <td>0.9962523424953412</td>\n",
       "      <td>[14, 6, 5, 3]</td>\n",
       "      <td>-150.00414282183715</td>\n",
       "      <td>[a:1][a:2]!@[NH1:3][C,c:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.39562014452426714</td>\n",
       "      <td>0.3888043941382421</td>\n",
       "      <td>0.40251512333759515</td>\n",
       "      <td>[16, 19, 20, 22]</td>\n",
       "      <td>-61.38963312922269</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0.026408724999889943</td>\n",
       "      <td>0.013841973955324827</td>\n",
       "      <td>0.03924319628256051</td>\n",
       "      <td>[7, 8, 9, 10]</td>\n",
       "      <td>0.005349331640332753</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>0.011319227988804137</td>\n",
       "      <td>-0.012464309920902417</td>\n",
       "      <td>0.035700147597479415</td>\n",
       "      <td>[0, 1, 16, 18]</td>\n",
       "      <td>-179.99253735514415</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>0.01114072330755745</td>\n",
       "      <td>-0.01263239662804397</td>\n",
       "      <td>0.03550998609413992</td>\n",
       "      <td>[20, 19, 16, 1]</td>\n",
       "      <td>179.9962690609747</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mol2</td>\n",
       "      <td>ZINC000159533726</td>\n",
       "      <td>3.4413750912962766</td>\n",
       "      <td>2.4294311373485353</td>\n",
       "      <td>nan</td>\n",
       "      <td>4</td>\n",
       "      <td>2.1206363671576907</td>\n",
       "      <td>1.2832134972295552</td>\n",
       "      <td>nan</td>\n",
       "      <td>[16, 1, 3, 4]</td>\n",
       "      <td>-120.00615479360211</td>\n",
       "      <td>[NH1:1][CX4:2]!@[CX3:3]=[O:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8752468812310884</td>\n",
       "      <td>0.773987448810736</td>\n",
       "      <td>0.9962523424953412</td>\n",
       "      <td>[14, 6, 5, 3]</td>\n",
       "      <td>-150.00414282183715</td>\n",
       "      <td>[a:1][a:2]!@[NH1:3][C,c:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.39562014452426714</td>\n",
       "      <td>0.3888043941382421</td>\n",
       "      <td>0.40251512333759515</td>\n",
       "      <td>[16, 19, 20, 22]</td>\n",
       "      <td>-61.38963312922269</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02694553547221107</td>\n",
       "      <td>0.014363236109127653</td>\n",
       "      <td>0.03979653843518938</td>\n",
       "      <td>[12, 8, 9, 11]</td>\n",
       "      <td>0.01971914685896898</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>0.011319227988804137</td>\n",
       "      <td>-0.012464309920902417</td>\n",
       "      <td>0.035700147597479415</td>\n",
       "      <td>[0, 1, 16, 18]</td>\n",
       "      <td>-179.99253735514415</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>0.01114072330755745</td>\n",
       "      <td>-0.01263239662804397</td>\n",
       "      <td>0.03550998609413992</td>\n",
       "      <td>[20, 19, 16, 1]</td>\n",
       "      <td>179.9962690609747</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Origin       Molecule_ID        Total_Energy  Energy_Lower_Bound  \\\n",
       "0    sdf  ZINC000159533726   3.440838280823955  2.4289098751947322   \n",
       "1   mol2  ZINC000159533726  3.4413750912962766  2.4294311373485353   \n",
       "\n",
       "  Energy_Upper_Bound Num_Torsion_Patterns    Torsion_1_Energy  \\\n",
       "0                nan                    4  2.1206363671576907   \n",
       "1                nan                    4  2.1206363671576907   \n",
       "\n",
       "  Torsion_1_Energy_Lower_Bound Torsion_1_Energy_Upper_Bound  \\\n",
       "0           1.2832134972295552                          nan   \n",
       "1           1.2832134972295552                          nan   \n",
       "\n",
       "  Torsion_1_Atom_Indices Torsion_1_Dihedral_Angle  \\\n",
       "0          [16, 1, 3, 4]      -120.00615479360211   \n",
       "1          [16, 1, 3, 4]      -120.00615479360211   \n",
       "\n",
       "        Torsion_1_SMARTS_Pattern Torsion_1_Hierarchy_Class Torsion_1_Method  \\\n",
       "0  [NH1:1][CX4:2]!@[CX3:3]=[O:4]                  specific            exact   \n",
       "1  [NH1:1][CX4:2]!@[CX3:3]=[O:4]                  specific            exact   \n",
       "\n",
       "  Torsion_1_Indices_Reversed Torsion_1_Rule_Index    Torsion_2_Energy  \\\n",
       "0                      False                    0  0.8752468812310884   \n",
       "1                      False                    0  0.8752468812310884   \n",
       "\n",
       "  Torsion_2_Energy_Lower_Bound Torsion_2_Energy_Upper_Bound  \\\n",
       "0            0.773987448810736           0.9962523424953412   \n",
       "1            0.773987448810736           0.9962523424953412   \n",
       "\n",
       "  Torsion_2_Atom_Indices Torsion_2_Dihedral_Angle    Torsion_2_SMARTS_Pattern  \\\n",
       "0          [14, 6, 5, 3]      -150.00414282183715  [a:1][a:2]!@[NH1:3][C,c:4]   \n",
       "1          [14, 6, 5, 3]      -150.00414282183715  [a:1][a:2]!@[NH1:3][C,c:4]   \n",
       "\n",
       "  Torsion_2_Hierarchy_Class Torsion_2_Method Torsion_2_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_2_Rule_Index     Torsion_3_Energy Torsion_3_Energy_Lower_Bound  \\\n",
       "0                    3  0.39562014452426714           0.3888043941382421   \n",
       "1                    3  0.39562014452426714           0.3888043941382421   \n",
       "\n",
       "  Torsion_3_Energy_Upper_Bound Torsion_3_Atom_Indices  \\\n",
       "0          0.40251512333759515       [16, 19, 20, 22]   \n",
       "1          0.40251512333759515       [16, 19, 20, 22]   \n",
       "\n",
       "  Torsion_3_Dihedral_Angle        Torsion_3_SMARTS_Pattern  \\\n",
       "0       -61.38963312922269  [!#1:1][CX4:2]!@[CX4:3][!#1:4]   \n",
       "1       -61.38963312922269  [!#1:1][CX4:2]!@[CX4:3][!#1:4]   \n",
       "\n",
       "  Torsion_3_Hierarchy_Class Torsion_3_Method Torsion_3_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_3_Rule_Index      Torsion_4_Energy Torsion_4_Energy_Lower_Bound  \\\n",
       "0                    2  0.026408724999889943         0.013841973955324827   \n",
       "1                    2   0.02694553547221107         0.014363236109127653   \n",
       "\n",
       "  Torsion_4_Energy_Upper_Bound Torsion_4_Atom_Indices  \\\n",
       "0          0.03924319628256051          [7, 8, 9, 10]   \n",
       "1          0.03979653843518938         [12, 8, 9, 11]   \n",
       "\n",
       "  Torsion_4_Dihedral_Angle    Torsion_4_SMARTS_Pattern  \\\n",
       "0     0.005349331640332753  [a:1][a:2]!@[NX3:3][!#1:4]   \n",
       "1      0.01971914685896898  [a:1][a:2]!@[NX3:3][!#1:4]   \n",
       "\n",
       "  Torsion_4_Hierarchy_Class Torsion_4_Method Torsion_4_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_4_Rule_Index      Torsion_5_Energy Torsion_5_Energy_Lower_Bound  \\\n",
       "0                    5  0.011319227988804137        -0.012464309920902417   \n",
       "1                    5  0.011319227988804137        -0.012464309920902417   \n",
       "\n",
       "  Torsion_5_Energy_Upper_Bound Torsion_5_Atom_Indices  \\\n",
       "0         0.035700147597479415         [0, 1, 16, 18]   \n",
       "1         0.035700147597479415         [0, 1, 16, 18]   \n",
       "\n",
       "  Torsion_5_Dihedral_Angle      Torsion_5_SMARTS_Pattern  \\\n",
       "0      -179.99253735514415  [*:1]~[CX4:2]!@[NX4:3]~[*:4]   \n",
       "1      -179.99253735514415  [*:1]~[CX4:2]!@[NX4:3]~[*:4]   \n",
       "\n",
       "  Torsion_5_Hierarchy_Class Torsion_5_Method Torsion_5_Indices_Reversed  \\\n",
       "0                   general            exact                      False   \n",
       "1                   general            exact                      False   \n",
       "\n",
       "  Torsion_5_Rule_Index     Torsion_6_Energy Torsion_6_Energy_Lower_Bound  \\\n",
       "0                    6  0.01114072330755745         -0.01263239662804397   \n",
       "1                    6  0.01114072330755745         -0.01263239662804397   \n",
       "\n",
       "  Torsion_6_Energy_Upper_Bound Torsion_6_Atom_Indices  \\\n",
       "0          0.03550998609413992        [20, 19, 16, 1]   \n",
       "1          0.03550998609413992        [20, 19, 16, 1]   \n",
       "\n",
       "  Torsion_6_Dihedral_Angle      Torsion_6_SMARTS_Pattern  \\\n",
       "0        179.9962690609747  [*:1]~[CX4:2]!@[NX4:3]~[*:4]   \n",
       "1        179.9962690609747  [*:1]~[CX4:2]!@[NX4:3]~[*:4]   \n",
       "\n",
       "  Torsion_6_Hierarchy_Class Torsion_6_Method Torsion_6_Indices_Reversed  \\\n",
       "0                   general            exact                      False   \n",
       "1                   general            exact                      False   \n",
       "\n",
       "  Torsion_6_Rule_Index  \n",
       "0                    1  \n",
       "1                    1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Molecule_ID</th>\n",
       "      <th>Total_Energy</th>\n",
       "      <th>Energy_Lower_Bound</th>\n",
       "      <th>Energy_Upper_Bound</th>\n",
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       "      <th>Torsion_1_Atom_Indices</th>\n",
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       "      <th>Torsion_1_SMARTS_Pattern</th>\n",
       "      <th>Torsion_1_Hierarchy_Class</th>\n",
       "      <th>Torsion_1_Method</th>\n",
       "      <th>Torsion_1_Indices_Reversed</th>\n",
       "      <th>Torsion_1_Rule_Index</th>\n",
       "      <th>Torsion_2_Energy</th>\n",
       "      <th>Torsion_2_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_2_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_2_Atom_Indices</th>\n",
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       "      <th>Torsion_2_Indices_Reversed</th>\n",
       "      <th>Torsion_2_Rule_Index</th>\n",
       "      <th>Torsion_3_Energy</th>\n",
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       "      <th>Torsion_3_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_3_Atom_Indices</th>\n",
       "      <th>Torsion_3_Dihedral_Angle</th>\n",
       "      <th>Torsion_3_SMARTS_Pattern</th>\n",
       "      <th>Torsion_3_Hierarchy_Class</th>\n",
       "      <th>Torsion_3_Method</th>\n",
       "      <th>Torsion_3_Indices_Reversed</th>\n",
       "      <th>Torsion_3_Rule_Index</th>\n",
       "      <th>Torsion_4_Energy</th>\n",
       "      <th>Torsion_4_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_4_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_4_Atom_Indices</th>\n",
       "      <th>Torsion_4_Dihedral_Angle</th>\n",
       "      <th>Torsion_4_SMARTS_Pattern</th>\n",
       "      <th>Torsion_4_Hierarchy_Class</th>\n",
       "      <th>Torsion_4_Method</th>\n",
       "      <th>Torsion_4_Indices_Reversed</th>\n",
       "      <th>Torsion_4_Rule_Index</th>\n",
       "      <th>Torsion_5_Energy</th>\n",
       "      <th>Torsion_5_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_5_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_5_Atom_Indices</th>\n",
       "      <th>Torsion_5_Dihedral_Angle</th>\n",
       "      <th>Torsion_5_SMARTS_Pattern</th>\n",
       "      <th>Torsion_5_Hierarchy_Class</th>\n",
       "      <th>Torsion_5_Method</th>\n",
       "      <th>Torsion_5_Indices_Reversed</th>\n",
       "      <th>Torsion_5_Rule_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sdf</td>\n",
       "      <td>ZINC000151228439</td>\n",
       "      <td>2.6254850156147613</td>\n",
       "      <td>2.1481317994215723</td>\n",
       "      <td>3.2248541322315853</td>\n",
       "      <td>4</td>\n",
       "      <td>1.2261404216241005</td>\n",
       "      <td>1.0624704033253995</td>\n",
       "      <td>1.4487026903383522</td>\n",
       "      <td>[16, 1, 3, 6]</td>\n",
       "      <td>160.0003287996878</td>\n",
       "      <td>[*:1]~[CX4:2]!@[NX4:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7072729781090777</td>\n",
       "      <td>0.5657264515554503</td>\n",
       "      <td>0.8932015389124958</td>\n",
       "      <td>[11, 10, 7, 9]</td>\n",
       "      <td>-120.00211031322509</td>\n",
       "      <td>[a:1][c:2]!@[CX4H0:3][N,O:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3450126608448396</td>\n",
       "      <td>0.338874330049487</td>\n",
       "      <td>0.3512114446836594</td>\n",
       "      <td>[3, 6, 7, 8]</td>\n",
       "      <td>60.001998575596204</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0.19399550580280375</td>\n",
       "      <td>0.133121574139351</td>\n",
       "      <td>0.26107092644386387</td>\n",
       "      <td>[26, 16, 1, 3]</td>\n",
       "      <td>-120.31533631847783</td>\n",
       "      <td>[a:1][c:2]!@[CX4H1:3][N,O:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1420856999880718</td>\n",
       "      <td>0.06072392664085746</td>\n",
       "      <td>0.23533248458078143</td>\n",
       "      <td>[20, 19, 18, 25]</td>\n",
       "      <td>0.2932336556874904</td>\n",
       "      <td>[*:1]~[cX3:2]!@[n:3]~[*:4]</td>\n",
       "      <td>general</td>\n",
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       "      <td>5</td>\n",
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       "      <th>1</th>\n",
       "      <td>mol2</td>\n",
       "      <td>ZINC000151228439</td>\n",
       "      <td>3.1921530742055166</td>\n",
       "      <td>2.7767434999098652</td>\n",
       "      <td>3.720129943770846</td>\n",
       "      <td>4</td>\n",
       "      <td>1.2261404216241005</td>\n",
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       "      <td>[16, 1, 3, 6]</td>\n",
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       "      <td>general</td>\n",
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       "      <td>0.7981722814216906</td>\n",
       "      <td>0.9413503909302561</td>\n",
       "      <td>[3, 1, 16, 26]</td>\n",
       "      <td>-120.31533631847783</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX3H0:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.7072729781090777</td>\n",
       "      <td>0.5657264515554503</td>\n",
       "      <td>0.8932015389124958</td>\n",
       "      <td>[11, 10, 7, 9]</td>\n",
       "      <td>-120.00211031322509</td>\n",
       "      <td>[a:1][c:2]!@[CX4H0:3][N,O:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3450126608448396</td>\n",
       "      <td>0.338874330049487</td>\n",
       "      <td>0.3512114446836594</td>\n",
       "      <td>[3, 6, 7, 8]</td>\n",
       "      <td>60.001998575596204</td>\n",
       "      <td>[!#1:1][CX4:2]!@[CX4:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "      <td>[20, 19, 18, 25]</td>\n",
       "      <td>0.2932336556874904</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "      <td>specific</td>\n",
       "      <td>exact</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  Origin       Molecule_ID        Total_Energy  Energy_Lower_Bound  \\\n",
       "0    sdf  ZINC000151228439  2.6254850156147613  2.1481317994215723   \n",
       "1   mol2  ZINC000151228439  3.1921530742055166  2.7767434999098652   \n",
       "\n",
       "   Energy_Upper_Bound Num_Torsion_Patterns    Torsion_1_Energy  \\\n",
       "0  3.2248541322315853                    4  1.2261404216241005   \n",
       "1   3.720129943770846                    4  1.2261404216241005   \n",
       "\n",
       "  Torsion_1_Energy_Lower_Bound Torsion_1_Energy_Upper_Bound  \\\n",
       "0           1.0624704033253995           1.4487026903383522   \n",
       "1           1.0624704033253995           1.4487026903383522   \n",
       "\n",
       "  Torsion_1_Atom_Indices Torsion_1_Dihedral_Angle  \\\n",
       "0          [16, 1, 3, 6]        160.0003287996878   \n",
       "1          [16, 1, 3, 6]        160.0003287996878   \n",
       "\n",
       "       Torsion_1_SMARTS_Pattern Torsion_1_Hierarchy_Class Torsion_1_Method  \\\n",
       "0  [*:1]~[CX4:2]!@[NX4:3]~[*:4]                   general            exact   \n",
       "1  [*:1]~[CX4:2]!@[NX4:3]~[*:4]                   general            exact   \n",
       "\n",
       "  Torsion_1_Indices_Reversed Torsion_1_Rule_Index    Torsion_2_Energy  \\\n",
       "0                      False                    1  0.7072729781090777   \n",
       "1                      False                    2  0.8655860990765392   \n",
       "\n",
       "  Torsion_2_Energy_Lower_Bound Torsion_2_Energy_Upper_Bound  \\\n",
       "0           0.5657264515554503           0.8932015389124958   \n",
       "1           0.7981722814216906           0.9413503909302561   \n",
       "\n",
       "  Torsion_2_Atom_Indices Torsion_2_Dihedral_Angle  \\\n",
       "0         [11, 10, 7, 9]      -120.00211031322509   \n",
       "1         [3, 1, 16, 26]      -120.31533631847783   \n",
       "\n",
       "           Torsion_2_SMARTS_Pattern Torsion_2_Hierarchy_Class  \\\n",
       "0      [a:1][c:2]!@[CX4H0:3][N,O:4]                  specific   \n",
       "1  [!#1:1][CX4:2]!@[CX3H0:3][!#1:4]                  specific   \n",
       "\n",
       "  Torsion_2_Method Torsion_2_Indices_Reversed Torsion_2_Rule_Index  \\\n",
       "0            exact                      False                    0   \n",
       "1            exact                      False                    3   \n",
       "\n",
       "     Torsion_3_Energy Torsion_3_Energy_Lower_Bound  \\\n",
       "0  0.3450126608448396            0.338874330049487   \n",
       "1  0.7072729781090777           0.5657264515554503   \n",
       "\n",
       "  Torsion_3_Energy_Upper_Bound Torsion_3_Atom_Indices  \\\n",
       "0           0.3512114446836594           [3, 6, 7, 8]   \n",
       "1           0.8932015389124958         [11, 10, 7, 9]   \n",
       "\n",
       "  Torsion_3_Dihedral_Angle        Torsion_3_SMARTS_Pattern  \\\n",
       "0       60.001998575596204  [!#1:1][CX4:2]!@[CX4:3][!#1:4]   \n",
       "1      -120.00211031322509    [a:1][c:2]!@[CX4H0:3][N,O:4]   \n",
       "\n",
       "  Torsion_3_Hierarchy_Class Torsion_3_Method Torsion_3_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_3_Rule_Index     Torsion_4_Energy Torsion_4_Energy_Lower_Bound  \\\n",
       "0                    2  0.19399550580280375            0.133121574139351   \n",
       "1                    1   0.3450126608448396            0.338874330049487   \n",
       "\n",
       "  Torsion_4_Energy_Upper_Bound Torsion_4_Atom_Indices  \\\n",
       "0          0.26107092644386387         [26, 16, 1, 3]   \n",
       "1           0.3512114446836594           [3, 6, 7, 8]   \n",
       "\n",
       "  Torsion_4_Dihedral_Angle        Torsion_4_SMARTS_Pattern  \\\n",
       "0      -120.31533631847783    [a:1][c:2]!@[CX4H1:3][N,O:4]   \n",
       "1       60.001998575596204  [!#1:1][CX4:2]!@[CX4:3][!#1:4]   \n",
       "\n",
       "  Torsion_4_Hierarchy_Class Torsion_4_Method Torsion_4_Indices_Reversed  \\\n",
       "0                  specific            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_4_Rule_Index     Torsion_5_Energy Torsion_5_Energy_Lower_Bound  \\\n",
       "0                    3   0.1420856999880718          0.06072392664085746   \n",
       "1                    0  0.03716316530509145         0.024284919846811026   \n",
       "\n",
       "  Torsion_5_Energy_Upper_Bound Torsion_5_Atom_Indices  \\\n",
       "0          0.23533248458078143       [20, 19, 18, 25]   \n",
       "1          0.05032883163365015       [20, 19, 18, 25]   \n",
       "\n",
       "  Torsion_5_Dihedral_Angle    Torsion_5_SMARTS_Pattern  \\\n",
       "0       0.2932336556874904  [*:1]~[cX3:2]!@[n:3]~[*:4]   \n",
       "1       0.2932336556874904  [a:1][a:2]!@[NX3:3][!#1:4]   \n",
       "\n",
       "  Torsion_5_Hierarchy_Class Torsion_5_Method Torsion_5_Indices_Reversed  \\\n",
       "0                   general            exact                      False   \n",
       "1                  specific            exact                      False   \n",
       "\n",
       "  Torsion_5_Rule_Index  \n",
       "0                    5  \n",
       "1                    5  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1, df_2, df_3 = create_dataframes(\"sdf.csv\", \"mol2.csv\")\n",
    "display(df_1)\n",
    "display(df_2)\n",
    "display(df_3)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just a side note, because of how the output data is structured, it gets really awkward and difficult to debug what is going on. Every line of the csv has a different amount of elements based on the torsions. I don't know what data structure should have been used, but this feels very unideal. \n",
    "\n",
    "However, the take away right now is the difference in total strain energy found. In these examples (which are very limited), our calculations are *very* close. Particularly for the first two. The last molecule is like .5 units off. I don't know how to estimate how much that matters, but I haven't really tried. I've just been focused on fixing them because the calculation is deterministic. Given the same input, we should get the same output. \n",
    "\n",
    "So now let's look at what is actually different between our outputs. I had some issues with sig figs in an earlier version of this, so I rounded to 8 places here. This function will select the unique columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_unique_columns_rounded(df):\n",
    "    # Reset the index of df\n",
    "    df = df.reset_index(drop=True)\n",
    "\n",
    "    # Round the DataFrame to the 8th decimal place\n",
    "    df_rounded = df.round(8)\n",
    "\n",
    "    # Select the first two columns \n",
    "    # of df_rounded\n",
    "    first_two_columns = df_rounded.iloc[:, :2]\n",
    "\n",
    "    # Select the rest of the columns where the rows have different values\n",
    "    unique_columns = df_rounded.iloc[:, 2:].loc[:, df_rounded.iloc[:, 2:].nunique() > 1]\n",
    "\n",
    "    # Concatenate the first two columns and the unique columns\n",
    "    df_unique = pd.concat([first_two_columns, unique_columns], axis=1)\n",
    "\n",
    "    # Return the new DataFrame\n",
    "    return df_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the shapes of the differences in the dataframes:\n",
      "(2, 9) (2, 9) (2, 32)\n"
     ]
    }
   ],
   "source": [
    "df_1_r8_unique = select_unique_columns_rounded(df_1)\n",
    "df_2_r8_unique = select_unique_columns_rounded(df_2)\n",
    "df_3_r8_unique = select_unique_columns_rounded(df_3)\n",
    "print(\"Here are the shapes of the differences in the dataframes:\")\n",
    "print(df_1_r8_unique.shape, df_2_r8_unique.shape, df_3_r8_unique.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Molecule_ID</th>\n",
       "      <th>Total_Energy</th>\n",
       "      <th>Energy_Lower_Bound</th>\n",
       "      <th>Torsion_7_Energy</th>\n",
       "      <th>Torsion_7_Energy_Lower_Bound</th>\n",
       "      <th>Torsion_7_Energy_Upper_Bound</th>\n",
       "      <th>Torsion_7_Atom_Indices</th>\n",
       "      <th>Torsion_7_Dihedral_Angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sdf</td>\n",
       "      <td>ZINC000191583186</td>\n",
       "      <td>5.605219996833657</td>\n",
       "      <td>5.056429357277703</td>\n",
       "      <td>0.02635434808368613</td>\n",
       "      <td>0.013789212541832876</td>\n",
       "      <td>0.03918709847466442</td>\n",
       "      <td>[18, 23, 24, 25]</td>\n",
       "      <td>-0.0040091720895600565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mol2</td>\n",
       "      <td>ZINC000191583186</td>\n",
       "      <td>5.621730380254799</td>\n",
       "      <td>5.0724614891320625</td>\n",
       "      <td>0.04286473150482756</td>\n",
       "      <td>0.029821344396192107</td>\n",
       "      <td>0.056205983956472805</td>\n",
       "      <td>[22, 23, 24, 26]</td>\n",
       "      <td>0.44585819818713934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Origin       Molecule_ID       Total_Energy  Energy_Lower_Bound  \\\n",
       "0    sdf  ZINC000191583186  5.605219996833657   5.056429357277703   \n",
       "1   mol2  ZINC000191583186  5.621730380254799  5.0724614891320625   \n",
       "\n",
       "      Torsion_7_Energy Torsion_7_Energy_Lower_Bound  \\\n",
       "0  0.02635434808368613         0.013789212541832876   \n",
       "1  0.04286473150482756         0.029821344396192107   \n",
       "\n",
       "  Torsion_7_Energy_Upper_Bound Torsion_7_Atom_Indices Torsion_7_Dihedral_Angle  \n",
       "0          0.03918709847466442       [18, 23, 24, 25]   -0.0040091720895600565  \n",
       "1         0.056205983956472805       [22, 23, 24, 26]      0.44585819818713934  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_1_r8_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference in energy is caused by different Torsion Indices and their Dihedral Angles.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Torsion_7_Atom_Indices</th>\n",
       "      <th>Torsion_7_Dihedral_Angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sdf</td>\n",
       "      <td>[18, 23, 24, 25]</td>\n",
       "      <td>-0.0040091720895600565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mol2</td>\n",
       "      <td>[22, 23, 24, 26]</td>\n",
       "      <td>0.44585819818713934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Origin Torsion_7_Atom_Indices Torsion_7_Dihedral_Angle\n",
       "0    sdf       [18, 23, 24, 25]   -0.0040091720895600565\n",
       "1   mol2       [22, 23, 24, 26]      0.44585819818713934"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The difference in energy is caused by different Torsion Indices and their Dihedral Angles.\")\n",
    "display(df_1_r8_unique[['Origin','Torsion_7_Atom_Indices', 'Torsion_7_Dihedral_Angle']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can find the SMARTs pattern associated with this in df_1:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Torsion_7_Atom_Indices</th>\n",
       "      <th>Torsion_7_Dihedral_Angle</th>\n",
       "      <th>Torsion_7_SMARTS_Pattern</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sdf</td>\n",
       "      <td>[18, 23, 24, 25]</td>\n",
       "      <td>-0.0040091720895600565</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mol2</td>\n",
       "      <td>[22, 23, 24, 26]</td>\n",
       "      <td>0.44585819818713934</td>\n",
       "      <td>[a:1][a:2]!@[NX3:3][!#1:4]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Origin Torsion_7_Atom_Indices Torsion_7_Dihedral_Angle  \\\n",
       "0    sdf       [18, 23, 24, 25]   -0.0040091720895600565   \n",
       "1   mol2       [22, 23, 24, 26]      0.44585819818713934   \n",
       "\n",
       "     Torsion_7_SMARTS_Pattern  \n",
       "0  [a:1][a:2]!@[NX3:3][!#1:4]  \n",
       "1  [a:1][a:2]!@[NX3:3][!#1:4]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bafflingly, the SMARTs is the same!\n",
      "We can try to visualize this, let's start with the mol2 file. However the results won't make sense.\n"
     ]
    }
   ],
   "source": [
    "print(\"We can find the SMARTs pattern associated with this in df_1:\")\n",
    "display(df_1[['Origin', 'Torsion_7_Atom_Indices', 'Torsion_7_Dihedral_Angle', 'Torsion_7_SMARTS_Pattern']])\n",
    "print(\"Bafflingly, the SMARTs is the same!\")\n",
    "print(\"We can try to visualize this, let's start with the mol2 file. However the results won't make sense.\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching atom indices via Chem.MolFromSmarts: (18, 23, 24, 26)\n",
      "Matching atom indices found from df_1: [22, 23, 24, 26], Origin: mol2\n",
      "Key, MOL2: ZINC000191583186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2a358d7b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the first molecule and its key from the mol2_mols dictionary\n",
    "key_H, mol_H = list(mol2_mols.items())[0]\n",
    "\n",
    "# Define the SMARTS pattern\n",
    "smarts = \"[a:1][a:2]!@[NX3:3][!#1:4]\"\n",
    "\n",
    "# Compute 2D coordinates for the molecule\n",
    "AllChem.Compute2DCoords(mol_H)\n",
    "\n",
    "# Find the atoms that match the SMARTS pattern\n",
    "patt = Chem.MolFromSmarts(smarts)\n",
    "match = mol_H.GetSubstructMatch(patt)\n",
    "\n",
    "# Print the tuple of matching atom indices\n",
    "print(f\"Matching atom indices via Chem.MolFromSmarts: {match}\")\n",
    "print(f\"Matching atom indices found from df_1: {df_1['Torsion_7_Atom_Indices'][1]}, Origin: {df_1['Origin'][1]}\")\n",
    "\n",
    "# Create a temporary copy of the molecule for highlighting\n",
    "mol_H_temp = Chem.Mol(mol_H)\n",
    "\n",
    "# Highlight the matching atoms on the temporary copy\n",
    "mol_H_temp.GetSubstructMatch(patt)\n",
    "\n",
    "# Display the key and the temporary molecule with the matching atoms highlighted\n",
    "print(f\"Key, MOL2: {key_H}\")\n",
    "display(mol_H_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an understandable kind of error because that SMARTs pattern could fit either aromatic carbon and connect all the way to N-O. It could also capture either oxygen 26 or oxygen 25. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching atom indices via Chem.MolFromSmarts: (18, 23, 24, 25)\n",
      "Matching atom indices found from df_1: [18, 23, 24, 25], Origin: sdf\n",
      "Key, SDF: ZINC000191583186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2a6667970>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the first molecule and its key from the sdf_mols dictionary\n",
    "key_H, sdf_H = list(sdf_mols.items())[0]\n",
    "\n",
    "# Define the SMARTS pattern\n",
    "smarts = \"[a:1][a:2]!@[NX3:3][!#1:4]\"\n",
    "\n",
    "# Compute 2D coordinates for the molecule\n",
    "AllChem.Compute2DCoords(sdf_H)\n",
    "\n",
    "# Find the atoms that match the SMARTS pattern\n",
    "patt = Chem.MolFromSmarts(smarts)\n",
    "match = sdf_H.GetSubstructMatch(patt)\n",
    "\n",
    "# Print the tuple of matching atom indices\n",
    "print(f\"Matching atom indices via Chem.MolFromSmarts: {match}\")\n",
    "print(f\"Matching atom indices found from df_1: {df_1['Torsion_7_Atom_Indices'][0]}, Origin: {df_1['Origin'][0]}\")\n",
    "\n",
    "# Create a temporary copy of the molecule for highlighting\n",
    "sdf_H_temp = Chem.Mol(sdf_H)\n",
    "\n",
    "# Highlight the matching atoms on the temporary copy\n",
    "sdf_H_temp.GetSubstructMatch(patt)\n",
    "\n",
    "# Display the key and the temporary molecule with the matching atoms highlighted\n",
    "print(f\"Key, SDF: {key_H}\")\n",
    "display(sdf_H_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My code does actually agree with what rdkit finds. \n",
    "\n",
    "From here, I am pretty lost on how to solve this. In reality, either match is correct (actually - both matches are more correct than either of our individual matches). Both matches are going to give different dihedral angles (because they truly are different atom coordinates). However teh code is deterministic. If you re-run the csv generation on the mol2 data or on the sdf data, you will always find this exact same discrepency. \n",
    "\n",
    "On one hand, this is a clear limitation of his method in some respects. If his torsion data is good, it should handle both of our cases and is his code should integrate using both. \n",
    "\n",
    "On the other hand, even if this is the correct appraoch because I haven't considered some aspect of chemistry or something, I am no closer to figuring out *why* our matches are different. \n",
    "\n",
    "You can extend this analysis to the other three molecules and for every single energy change there is. It's very laborious/tedious. I can do this given more time. In the meanwhile, I've tried to just debug whatever is going on his code that I thought could cause this. Here they are: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecule in sdf: ZINC000191583186\n",
      "Molecule in mol2: ZINC000191583186\n",
      "Difference found in element 0 of indeces for molecule ZINC000191583186\n",
      "TL_customSDF: [18, 23, 24, 25], TL_mol2: [22, 23, 24, 26]\n",
      "No differences found in smarts for molecule ZINC000191583186\n",
      "Molecule in sdf: ZINC000159533726\n",
      "Molecule in mol2: ZINC000159533726\n",
      "Difference found in element 2 of indeces for molecule ZINC000159533726\n",
      "TL_customSDF: [7, 8, 9, 10], TL_mol2: [12, 8, 9, 11]\n",
      "No differences found in smarts for molecule ZINC000159533726\n",
      "Molecule in sdf: ZINC000151228439\n",
      "Molecule in mol2: ZINC000151228439\n",
      "Difference found in element 0 of indeces for molecule ZINC000151228439\n",
      "TL_customSDF: [3, 6, 7, 8], TL_mol2: [20, 19, 18, 25]\n",
      "Difference found in element 1 of indeces for molecule ZINC000151228439\n",
      "TL_customSDF: [11, 10, 7, 9], TL_mol2: [3, 6, 7, 8]\n",
      "Difference found in element 2 of indeces for molecule ZINC000151228439\n",
      "TL_customSDF: [26, 16, 1, 3], TL_mol2: [3, 1, 16, 26]\n",
      "Difference found in element 3 of indeces for molecule ZINC000151228439\n",
      "TL_customSDF: [20, 19, 18, 25], TL_mol2: [11, 10, 7, 9]\n",
      "Difference found in element 0 of smarts for molecule ZINC000151228439\n",
      "TL_customSDF: [!#1:1][CX4:2]!@[CX4:3][!#1:4], TL_mol2: [a:1][a:2]!@[NX3:3][!#1:4]\n",
      "Difference found in element 1 of smarts for molecule ZINC000151228439\n",
      "TL_customSDF: [a:1][c:2]!@[CX4H0:3][N,O:4], TL_mol2: [!#1:1][CX4:2]!@[CX4:3][!#1:4]\n",
      "Difference found in element 2 of smarts for molecule ZINC000151228439\n",
      "TL_customSDF: [a:1][c:2]!@[CX4H1:3][N,O:4], TL_mol2: [!#1:1][CX4:2]!@[CX3H0:3][!#1:4]\n",
      "Difference found in element 3 of smarts for molecule ZINC000151228439\n",
      "TL_customSDF: [*:1]~[cX3:2]!@[n:3]~[*:4], TL_mol2: [a:1][c:2]!@[CX4H0:3][N,O:4]\n"
     ]
    }
   ],
   "source": [
    "# What are ALL the atom indeces and SMARTs that differ between the two files?\n",
    "%run debug_TL_lookup.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecule in sdf: ZINC000191583186\n",
      "Molecule in mol2: ZINC000191583186\n",
      "Difference found: (18, 23, 24, 25) != (18, 23, 24, 26)\n",
      "Difference found: (22, 23, 24, 25) != (22, 23, 24, 26)\n",
      "Molecule in sdf: ZINC000159533726\n",
      "Molecule in mol2: ZINC000159533726\n",
      "Difference found: (7, 8, 9, 10) != (7, 8, 9, 11)\n",
      "Difference found: (12, 8, 9, 10) != (12, 8, 9, 11)\n",
      "Molecule in sdf: ZINC000151228439\n",
      "Molecule in mol2: ZINC000151228439\n",
      "Difference found: (9, 7, 6, 3) != (20, 19, 18, 17)\n",
      "Difference found: (3, 6, 7, 8) != (20, 19, 18, 25)\n",
      "Difference found: (3, 6, 7, 9) != (24, 19, 18, 17)\n",
      "Difference found: (3, 6, 7, 10) != (24, 19, 18, 25)\n",
      "Difference found: (11, 10, 7, 6) != (9, 7, 6, 3)\n",
      "Difference found: (11, 10, 7, 8) != (3, 6, 7, 8)\n",
      "Difference found: (15, 10, 7, 6) != (3, 6, 7, 9)\n",
      "Difference found: (15, 10, 7, 8) != (3, 6, 7, 10)\n",
      "Difference found: (11, 10, 7, 9) != (0, 1, 16, 26)\n",
      "Difference found: (15, 10, 7, 9) != (3, 1, 16, 26)\n",
      "Difference found: (11, 10, 7, 6) != (0, 1, 16, 26)\n",
      "Difference found: (11, 10, 7, 8) != (3, 1, 16, 26)\n",
      "Difference found: (11, 10, 7, 9) != (11, 10, 7, 6)\n",
      "Difference found: (15, 10, 7, 6) != (11, 10, 7, 8)\n",
      "Difference found: (15, 10, 7, 8) != (15, 10, 7, 6)\n",
      "Difference found: (15, 10, 7, 9) != (15, 10, 7, 8)\n",
      "Difference found: (17, 16, 1, 3) != (11, 10, 7, 9)\n",
      "Difference found: (26, 16, 1, 3) != (15, 10, 7, 9)\n",
      "Difference found: (20, 19, 18, 17) != (11, 10, 7, 6)\n",
      "Difference found: (20, 19, 18, 25) != (11, 10, 7, 8)\n",
      "Difference found: (24, 19, 18, 17) != (11, 10, 7, 9)\n",
      "Difference found: (24, 19, 18, 25) != (15, 10, 7, 6)\n",
      "Difference found: (3, 6, 7, 8) != (15, 10, 7, 8)\n",
      "Difference found: (3, 6, 7, 9) != (15, 10, 7, 9)\n",
      "Difference found: (3, 6, 7, 10) != (3, 6, 7, 8)\n",
      "Difference found: (0, 1, 16, 26) != (3, 6, 7, 9)\n",
      "Difference found: (0, 1, 16, 17) != (3, 6, 7, 10)\n",
      "Difference found: (3, 1, 16, 26) != (0, 1, 16, 26)\n",
      "Difference found: (3, 1, 16, 17) != (0, 1, 16, 17)\n",
      "Difference found: (6, 7, 10, 15) != (3, 1, 16, 26)\n",
      "Difference found: (6, 7, 10, 11) != (3, 1, 16, 17)\n",
      "Difference found: (8, 7, 10, 15) != (6, 7, 10, 15)\n",
      "Difference found: (8, 7, 10, 11) != (6, 7, 10, 11)\n",
      "Difference found: (9, 7, 10, 15) != (8, 7, 10, 15)\n",
      "Difference found: (9, 7, 10, 11) != (8, 7, 10, 11)\n",
      "Difference found: (0, 1, 3, 6) != (9, 7, 10, 15)\n",
      "Difference found: (7, 6, 3, 1) != (9, 7, 10, 11)\n",
      "Difference found: (16, 1, 3, 6) != (0, 1, 3, 6)\n",
      "Difference found: (20, 19, 18, 17) != (7, 6, 3, 1)\n",
      "Difference found: (20, 19, 18, 25) != (16, 1, 3, 6)\n",
      "Difference found: (24, 19, 18, 17) != (20, 19, 18, 17)\n",
      "Difference found: (24, 19, 18, 25) != (20, 19, 18, 25)\n",
      "Difference found: (0, 1, 3, 6) != (24, 19, 18, 17)\n",
      "Difference found: (1, 3, 6, 7) != (24, 19, 18, 25)\n"
     ]
    }
   ],
   "source": [
    "# His code does complicated stuff to filter all the potential matches a molecule could have with a given SMARTs to just one that he uses for energy. What does the raw match data look like? \n",
    "%run debug_tp_match.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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