dominicrufa/tm_jak2_analysis.ipynb

## tm_jak2_analysis.ipynb

      
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## tm_tyk2_analysis.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "880a42cc-a8ba-4c66-b957-61597d612b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "import pickle\n",
    "import typing\n",
    "import os\n",
    "import warnings\n",
    "import yaml\n",
    "\n",
    "from openff.toolkit.topology import Molecule\n",
    "\n",
    "from timemachine.constants import DEFAULT_TEMP, DEFAULT_KT, KCAL_TO_KJ\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from openmm import unit\n",
    "kB = unit.BOLTZMANN_CONSTANT_kB * unit.AVOGADRO_CONSTANT_NA\n",
    "kT = kB * 300. * unit.kelvin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5c460ec6-cd93-4d41-8856-5264e8ea67b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_edges_txt(edges_txt_file: str, **unused_kwargs) -> typing.Tuple[str]:\n",
    "    with open(edges_txt_file) as file:\n",
    "        lines = file.readlines()\n",
    "        lines = [line.rstrip() for line in lines]\n",
    "    as_tuple = [q.split() for q in lines]\n",
    "    return as_tuple\n",
    "\n",
    "def find_files_with_string(directory, string):\n",
    "    # List to store files that include the specified string\n",
    "    matching_files = []\n",
    "\n",
    "    # Iterate over files in the directory\n",
    "    for filename in os.listdir(directory):\n",
    "        # Check if the specified string is present in the filename\n",
    "        if string in filename:\n",
    "            # If the string is found, add the filename to the list\n",
    "            matching_files.append(filename)\n",
    "\n",
    "    return matching_files\n",
    "\n",
    "def find_line_with_string(file_path, search_string):\n",
    "    try:\n",
    "        with open(file_path, \"r\") as file:\n",
    "            # Read the file line by line\n",
    "            for line in file:\n",
    "                # Check if the search string is in the line\n",
    "                if search_string in line:\n",
    "                    return line.strip()  # Return the line without leading/trailing whitespace\n",
    "    except FileNotFoundError:\n",
    "        return \"File not found\"\n",
    "    return \"String not found\"\n",
    "\n",
    "def build_data_dict_from_outs(directory: str):\n",
    "    search_string = '.out'\n",
    "    matching_files = find_files_with_string(directory, search_string)\n",
    "    matching_files_path = [os.path.join(directory, f) for f in matching_files]\n",
    "    data_dict = {}\n",
    "    for match_file in matching_files_path:\n",
    "        try:\n",
    "            query_line = find_line_with_string(match_file, 'finished')\n",
    "            split_line = query_line.split()\n",
    "            #print(query_line.split())\n",
    "            if split_line[0] == 'finished:':\n",
    "                mol1, mol2 = split_line[1], split_line[3]\n",
    "                comp_ddG = np.array([float(split_line[7]), float(split_line[9])]) / KCAL_TO_KJ\n",
    "                solv_ddG = np.array([float(split_line[12]), float(split_line[14])]) / KCAL_TO_KJ\n",
    "                total_ddG = np.array([float(split_line[17]), float(split_line[19])]) / KCAL_TO_KJ\n",
    "                data_dict[(mol1, mol2)] = {\n",
    "                    'comp': comp_ddG,\n",
    "                    'solv': solv_ddG,\n",
    "                    'total': total_ddG\n",
    "                }\n",
    "        except Exception as e:\n",
    "            print(match_file, e)\n",
    "    return data_dict\n",
    "\n",
    "# this is what ivan used to query exptl free energies\n",
    "\n",
    "def read_sddata(filename, name_sdtag=None):\n",
    "    \"\"\"\n",
    "    Read molecular data from an SD file.\n",
    "\n",
    "    This function reads molecular data, including properties, from an SD file.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    filename : str\n",
    "        The path to the SDF file containing molecular data.\n",
    "    name_sdtag : str, optional\n",
    "        The SD tag name to use as the identifier for each molecule.\n",
    "        If provided, the molecules will be stored in the returned dictionary using the values\n",
    "        of this SD tag as keys. If not provided, the 'name' SD tag will be used as the key.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        A dictionary containing the molecular data. The keys are either the values of the specified\n",
    "        SD tag (if `name_sdtag` is provided) or the 'name' SD tag. The values are dictionaries\n",
    "        containing the properties associated with each molecule.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    - The 'openff.toolkit.Molecule' class is used to read the molecular data from the SD file.\n",
    "    - If no `name_sdtag` is provided, the 'name' SD tag will be used by default as the key in the\n",
    "      returned dictionary.\n",
    "\n",
    "    Example\n",
    "    -------\n",
    "    data = read_sddata('molecules.sdf', name_sdtag='Molecule_ID')\n",
    "    print(data['mol123'])  # Display properties of the molecule with Molecule_ID 'mol123'\n",
    "    \"\"\"\n",
    "    read_molecules = Molecule.from_file(filename, allow_undefined_stereo=True)\n",
    "    if name_sdtag:\n",
    "        molecules = {molecule.properties[name_sdtag]: molecule.properties for molecule in read_molecules}\n",
    "    else:\n",
    "        molecules = {molecule.name: molecule.properties for molecule in read_molecules}\n",
    "\n",
    "    return molecules\n",
    "\n",
    "\n",
    "def get_experimental_estimates(sdfile: str, pic50_sdtag: str=\"pChEMBL Value\",\n",
    "                               pic50_error_sdtag: str=\"pChEMBL Error\"):\n",
    "    \"\"\"\n",
    "    Compute experimental estimates of binding free energies and errors from an SD file.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sdfile : str\n",
    "        The path to the SD file containing ligand data.\n",
    "    pic50_sdtag : str, optional\n",
    "        The SD tag for the pIC50 values (default is \"pChEMBL Value\").\n",
    "    pic50_error_sdtag : str, optional\n",
    "        The SD tag for the pIC50 error values (default is \"pChEMBL Error\").\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    Dict[str, Dict[str, float]]\n",
    "        A dictionary containing the computed experimental estimates for each ligand.\n",
    "        Each entry in the dictionary has the following structure:\n",
    "        {\n",
    "            ligand_name: {\n",
    "                'dg_exp': <experimental binding free energy in kcal/mol>,\n",
    "                'ddg_exp': <experimental binding free energy error in kcal/mol>\n",
    "            },\n",
    "            ...\n",
    "        }\n",
    "\n",
    "    Raises\n",
    "    ------\n",
    "    KeyError\n",
    "        If the specified SD tag for pIC50 values or pIC50 error values is not found in the SD file.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    This function reads ligand data from an SD file and computes experimental estimates of binding free energies\n",
    "    and errors using the provided pIC50 values and pIC50 error values. The computed values are returned as a dictionary\n",
    "    where each key corresponds to a ligand name and the values are dictionaries containing the computed\n",
    "    experimental estimates.\n",
    "\n",
    "    If the specified SD tag for pIC50 error values is not found in the SD file, the function uses a default value\n",
    "    of 0.3 kcal/mol for the error estimate and emits a warning.\n",
    "\n",
    "    Example\n",
    "    -------\n",
    "    >>> estimates = get_experimental_estimates(\"ligands.sdf\")\n",
    "    >>> print(estimates['ligand1'])\n",
    "    {'dg_exp': -7.21, 'ddg_exp': 0.3}\n",
    "    \"\"\"\n",
    "    kT = kB * 300 * unit.kelvin # thermal energy at 300 K in J/mol\n",
    "\n",
    "    ligands_data = read_sddata(sdfile)\n",
    "\n",
    "    results = {}\n",
    "\n",
    "    for name, metadata in ligands_data.items():\n",
    "        # Compute experimental affinity in kcal/mol\n",
    "        ligand_name = name\n",
    "        dg_exp = - kT.value_in_unit(unit.kilocalorie_per_mole) * np.log(10) * float(metadata[pic50_sdtag])\n",
    "        try:\n",
    "            ddg_exp = - kT.value_in_unit(unit.kilocalorie_per_mole) * np.log(10) * float(metadata[pic50_error_sdtag])\n",
    "        except KeyError:\n",
    "            warnings.warn(f\"No SDtag '{pic50_error_sdtag}' for pIC50 error found, fixing to default value of 0.3\")\n",
    "            ddg_exp = 0.3 # estimate\n",
    "        results[name] = {\"dg_exp\": dg_exp, \"ddg_exp\": ddg_exp}\n",
    "\n",
    "    return results\n",
    "\n",
    "def load_exp_data_from_yml(yml_filepath):\n",
    "    \"\"\"load experimental free energy from a plbenchmark/yaml file;\n",
    "    do this when the ligands in .sdf don't have free energy data annotations...\n",
    "    \"\"\"\n",
    "    unit_conversions = { 'M' : 1.0, 'mM' : 1e-3, 'uM' : 1e-6, 'nM' : 1e-9, 'pM' : 1e-12, 'fM' : 1e-15 }\n",
    "    with open(yml_filepath, \"r\") as yaml_file:\n",
    "        yaml_data = yaml.load(yaml_file, Loader=yaml.FullLoader)\n",
    "    exp_dict = {}\n",
    "    for key, val in yaml_data.items():\n",
    "        _data = val['measurement']\n",
    "        _val = _data['value']\n",
    "        _unit = _data['unit']\n",
    "        value_to_molar= unit_conversions[_unit]\n",
    "        dg_exp = kT.value_in_unit(unit.kilocalorie_per_mole) * np.log(_val * value_to_molar)\n",
    "        ddg_exp = kT.value_in_unit(unit.kilocalorie_per_mole) * 0.3\n",
    "        exp_dict[key] = {\"dg_exp\": dg_exp, \"ddg_exp\": ddg_exp}\n",
    "    return exp_dict\n",
    "\n",
    "def annotate_data_dict_w_exp(data_dict, exp_dict, rewrite_ligands_to_idxs=True):\n",
    "    ligand_names_aslist = list(exp_dict.keys())\n",
    "    name_to_idx = {ligname: idx for idx, ligname in enumerate(ligand_names_aslist)}\n",
    "    new_data_dict = {}\n",
    "    for lig_pair, data in data_dict.items():\n",
    "        mol1, mol2 = lig_pair\n",
    "        dg_exp1, dg_exp_err1 = exp_dict[mol1]['dg_exp'], exp_dict[mol1]['ddg_exp']\n",
    "        dg_exp2, dg_exp_err2 = exp_dict[mol2]['dg_exp'], exp_dict[mol2]['ddg_exp']\n",
    "        diff_dg_exp = dg_exp2 - dg_exp1\n",
    "        diff_dg_exp_err = np.linalg.norm(np.array([dg_exp_err1, dg_exp_err2]))\n",
    "\n",
    "        new_val = {key: val for key, val in data.items()}\n",
    "        new_val['exp'] = np.array([diff_dg_exp, diff_dg_exp_err])\n",
    "        if rewrite_ligands_to_idxs:\n",
    "            lig_pair_as_idx = (name_to_idx[mol1], name_to_idx[mol2])\n",
    "        else: # do nothing\n",
    "            lig_pair_as_idx = (mol1, mol2)\n",
    "        new_data_dict[lig_pair_as_idx] = new_val\n",
    "    return new_data_dict\n",
    "\n",
    "def retrieve_exp_data_aslist(exp_dict):\n",
    "    ligand_names_aslist = list(exp_dict.keys())\n",
    "    name_to_idx = {ligname: idx for idx, ligname in enumerate(ligand_names_aslist)}\n",
    "    idx_to_name = {val: key for key,val in name_to_idx.items()}\n",
    "    out_list = [exp_dict[idx_to_name[i]]['dg_exp'] for i in range(len(ligand_names_aslist))]\n",
    "    return np.array(out_list)\n",
    "\n",
    "def node_vals_and_errs_inputs_from_my_data(exp_dict, annotated_data_dict):\n",
    "    ligand_names_aslist = list(exp_dict.keys())\n",
    "    name_to_idx = {ligname: idx for idx, ligname in enumerate(ligand_names_aslist)}\n",
    "    idx_to_name = {val: key for key,val in name_to_idx.items()}\n",
    "    \n",
    "    edge_idxs = np.array(list(annotated_data_dict.keys()))\n",
    "    all_edge_diffs = np.array([val['total'] for val in annotated_data_dict.values()])\n",
    "    edge_diffs = all_edge_diffs[:,0]\n",
    "    edge_stddevs = all_edge_diffs[:,1]\n",
    "    ref_node_idxs = np.arange(len(exp_dict))\n",
    "    ref_node_vals = np.array([exp_dict[idx_to_name[i]]['dg_exp'] for i in ref_node_idxs])\n",
    "    ref_node_stddevs = np.array([exp_dict[idx_to_name[i]]['ddg_exp'] for i in ref_node_idxs])\n",
    "    dgs, dg_errs = infer_node_vals_and_errs(edge_idxs, edge_diffs, edge_stddevs, ref_node_idxs, ref_node_vals, ref_node_stddevs)\n",
    "    return (edge_idxs, edge_diffs, edge_stddevs, ref_node_idxs, ref_node_vals, ref_node_stddevs)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "547d919f-dd29-42b0-8f17-2d9ba756449e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_standard_with_errors(calc_dGs, exp_dGs, calc_dG_err, exp_dG_err, \n",
    "                              xlabel = 'calc dG [kcal/mol]',\n",
    "                              ylabel = 'exp dG [kcal/mol]',\n",
    "                              title = ''):\n",
    "    all_concat = np.concatenate([calc_dGs, exp_dGs])\n",
    "    minval, maxval = np.min(all_concat), np.max(all_concat)\n",
    "    x_ref = np.linspace(minval - 1, maxval + 1, 1000)\n",
    "    y_ref = x_ref\n",
    "    y_ref_down = y_ref - 1.\n",
    "    y_ref_up = y_ref + 1.\n",
    "    \n",
    "    rmse = np.sqrt(np.mean(np.square(calc_dGs - exp_dGs))) \n",
    "    plt.errorbar(calc_dGs, exp_dGs, xerr = calc_dG_err, yerr = exp_dG_err, ls='none', marker='o', label=f\"RMSE: {rmse}\")\n",
    "    plt.plot(x_ref, y_ref, color='k')\n",
    "    plt.fill_between(x_ref, y_ref_down, y_ref_up, alpha=0.25, label=f\"+/- 1 kcal/mol\")\n",
    "    \n",
    "    plt.xlim(minval - 1, maxval + 1)\n",
    "    plt.ylim(minval - 1, maxval + 1)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.title(title)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d423b3a-e309-4051-80e9-afa12c2a6a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timemachine.fe.mle import infer_node_vals, infer_node_vals_and_errs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6d4e512c-f20c-471e-adfb-3c377a43604f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_to_query = '/data1/choderaj/rufad/tm/tyk2_off1_orig/'\n",
    "orig_data_dict = build_data_dict_from_outs(dir_to_query)\n",
    "orig_exp_data = load_exp_data_from_yml('tyk2_ligands.yml')\n",
    "orig_annotated_data = annotate_data_dict_w_exp(orig_data_dict, orig_exp_data, rewrite_ligands_to_idxs=True)\n",
    "orig_my_inputs = node_vals_and_errs_inputs_from_my_data(orig_exp_data, orig_annotated_data)\n",
    "orig_dgs, orig_dg_errs = infer_node_vals_and_errs(*orig_my_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2a4dd86c-9edb-4782-8883-c6ecb811308a",
   "metadata": {},
   "outputs": [],
   "source": [
    "off_dir_to_query = '/data1/choderaj/rufad/tm/tyk2_off1/'\n",
    "off_data_dict = build_data_dict_from_outs(off_dir_to_query)\n",
    "off_exp_data = load_exp_data_from_yml('tyk2_ligands.yml')\n",
    "off_annotated_data = annotate_data_dict_w_exp(off_data_dict, off_exp_data, rewrite_ligands_to_idxs=True)\n",
    "off_my_inputs = node_vals_and_errs_inputs_from_my_data(off_exp_data, off_annotated_data)\n",
    "off_dgs, off_dg_errs = infer_node_vals_and_errs(*off_my_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c2008215-f330-4e0f-9284-936ff0f6b0f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -9.50848512,  -9.0209135 ,  -9.03525374, -10.52903956,\n",
       "       -10.70112374,  -9.55628631,  -8.84404913,  -9.85982351,\n",
       "        -9.82636276, -10.59835141, -11.56632436, -11.42053108,\n",
       "       -10.57923092])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_dgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1c37fe0b-b208-47ec-bf33-e2fe99a67c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -9.96572142,  -9.85099876,  -7.55654384,  -9.47575977,\n",
       "       -10.70185913,  -9.611993  ,  -8.9595074 , -10.3887616 ,\n",
       "       -10.91696436, -11.13445948, -11.29698337, -11.0197368 ,\n",
       "       -10.16648621])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "off_dgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dc4b728f-d5b9-4355-abd1-feff5d8ccafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_standard_with_errors(orig_dgs, orig_my_inputs[4], orig_dg_errs, orig_my_inputs[-1], title=f\"out of box tm: tyk2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3cb65eb8-ceac-4e9e-983d-1c31c2bfda38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_standard_with_errors(off_dgs, off_my_inputs[4], off_dg_errs, off_my_inputs[-1], title = f\"modified tm: tyk2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "de7bdb77-3392-4b4a-b9e6-b289edbc04f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# maybe advisable to plot correlation of ddGs for two versions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "da2f88de-5cae-463e-a466-c92c5590aa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddG_data = []\n",
    "for key, val in orig_annotated_data.items():\n",
    "    orig_data = val['total']\n",
    "    off_data = off_annotated_data[key]['total']\n",
    "    ddG_data.append(np.concatenate([orig_data, off_data]))\n",
    "ddG_data = np.array(ddG_data)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ac9be436-ca57-4486-b6b6-81f78f91f5f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_standard_with_errors(ddG_data[:,0], ddG_data[:,2], ddG_data[:,1], ddG_data[:,3], \n",
    "                          xlabel=f\"unmodified tm rel dGs [kcal/mol]\",\n",
    "                          ylabel=f\"modified tm rel dGs [kcal/mol]\",\n",
    "                          title = f\"correlation of rel dGs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c4f05c87-48c4-443d-9aa2-9ebecb423ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# interesting that there is 1 big offender...let's try to pick it out as the largest deviator..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "86f8a2b8-4bbb-4155-a639-675d84208752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(ddG_data[:,0] - ddG_data[:,2]).argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c69ff1a4-aad9-483a-9dab-7a5352e15766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(9, 6): {'comp': array([12.44502868,  0.07648184]),\n",
       "  'solv': array([10.69072658,  0.06692161]),\n",
       "  'total': array([1.7543021 , 0.10038241]),\n",
       "  'exp': array([0.20558054, 0.25292981])},\n",
       " (10, 6): {'comp': array([41.0707457 ,  0.05975143]),\n",
       "  'solv': array([38.34608031,  0.05497132]),\n",
       "  'total': array([2.72227533, 0.08126195]),\n",
       "  'exp': array([2.72108765, 0.25292981])},\n",
       " (12, 6): {'comp': array([29.35946463,  0.06453155]),\n",
       "  'solv': array([27.62428298,  0.05736138]),\n",
       "  'total': array([1.73518164, 0.08604207]),\n",
       "  'exp': array([1.99152012, 0.25292981])},\n",
       " (7, 6): {'comp': array([16.29063098,  0.07409178]),\n",
       "  'solv': array([15.2748566 ,  0.06453155]),\n",
       "  'total': array([1.01577438, 0.09799235]),\n",
       "  'exp': array([-0.02433649,  0.25292981])},\n",
       " (2, 6): {'comp': array([9.84703633, 0.08126195]),\n",
       "  'solv': array([9.65344168, 0.06931166]),\n",
       "  'total': array([0.19120459, 0.10755258]),\n",
       "  'exp': array([-0.74684877,  0.25292981])},\n",
       " (0, 6): {'comp': array([11.50095602,  0.06692161]),\n",
       "  'solv': array([10.83652008,  0.05736138]),\n",
       "  'total': array([0.66443595, 0.08843212]),\n",
       "  'exp': array([0.54625705, 0.25292981])},\n",
       " (8, 6): {'comp': array([9.05353728, 0.08365201]),\n",
       "  'solv': array([8.07122371, 0.06931166]),\n",
       "  'total': array([0.98231358, 0.10994264]),\n",
       "  'exp': array([1.54421698, 0.25292981])},\n",
       " (4, 6): {'comp': array([40.55210325,  0.05736138]),\n",
       "  'solv': array([38.69502868,  0.05258126]),\n",
       "  'total': array([1.85707457, 0.07887189]),\n",
       "  'exp': array([2.33219663, 0.25292981])},\n",
       " (3, 6): {'comp': array([21.19024857,  0.08843212]),\n",
       "  'solv': array([19.50525813,  0.07409178]),\n",
       "  'total': array([1.68499044, 0.11472275]),\n",
       "  'exp': array([0.55880828, 0.25292981])},\n",
       " (11, 6): {'comp': array([39.97848948,  0.06453155]),\n",
       "  'solv': array([37.40200765,  0.05736138]),\n",
       "  'total': array([2.57648184, 0.08604207]),\n",
       "  'exp': array([2.29605458, 0.25292981])},\n",
       " (1, 6): {'comp': array([13.36759082,  0.07887189]),\n",
       "  'solv': array([13.19072658,  0.06214149]),\n",
       "  'total': array([0.17686424, 0.10038241]),\n",
       "  'exp': array([0.78797965, 0.25292981])},\n",
       " (5, 6): {'comp': array([-1.09703633,  0.0501912 ]),\n",
       "  'solv': array([-1.80688337,  0.0501912 ]),\n",
       "  'total': array([0.71223709, 0.07170172]),\n",
       "  'exp': array([0.70142776, 0.25292981])}}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_annotated_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8835872c-4969-451a-b97e-7d96b149518b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# perhaps we can inspect ligand 2 -> 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "92c16ec8-6615-4d9e-9e0e-50966957b234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lig_ejm_31': {'dg_exp': -9.63332098803645, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_42': {'dg_exp': -9.875043584905292, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_43': {'dg_exp': -8.34021516285796, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_45': {'dg_exp': -9.645872215361504, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_46': {'dg_exp': -11.419260567562732, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_47': {'dg_exp': -9.788491692432988, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_48': {'dg_exp': -9.087063934676417, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_50': {'dg_exp': -9.062727442269358, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_54': {'dg_exp': -10.631280917333855, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_ejm_55': {'dg_exp': -9.292644479549626, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_jmc_23': {'dg_exp': -11.808151583877756, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_jmc_27': {'dg_exp': -11.3831185155671, 'ddg_exp': 0.17884838327767483},\n",
       " 'lig_jmc_28': {'dg_exp': -11.078584059075908, 'ddg_exp': 0.17884838327767483}}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "off_exp_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "baebf46f-c56e-4252-96b7-83fbbeb73ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lig ejm 43 -> 48..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d5cdf253-5e8f-423c-b57c-01bb12005ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets take this, compute the energy of the single topology... and its consistency..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7bfeb338-2995-44dd-9eeb-f92346c221af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from importlib import resources\n",
    "from typing import no_type_check\n",
    "\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "import pytest\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "\n",
    "from timemachine import potentials\n",
    "from timemachine.fe import topology\n",
    "from timemachine.fe.topology import BaseTopology, DualTopology, DualTopologyMinimization\n",
    "from timemachine.fe.utils import get_mol_name, get_romol_conf, read_sdf, set_romol_conf\n",
    "from timemachine.ff import Forcefield, make_mol_omm_sys\n",
    "from timemachine.ff.handlers.openmm_deserializer import deserialize_system\n",
    "from timemachine.ff.handlers.bonded import annotate_mol_sys_torsions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "69cad27e-c77d-4473-b3c6-53e97d10eda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ligand_file = '/data1/choderaj/rufad/tm/tyk2_off1/ligands.sdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "235a7bfb-3a76-4b37-803c-66dfeae617e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define or load a molecule of interest via the Open Force Field toolkit\n",
    "from openff.toolkit.topology import Molecule\n",
    "\n",
    "mols = read_sdf(ligand_file) # read mols from sdf\n",
    "mol_dict = {get_mol_name(mol): mol for mol in mols} # make a mol dict\n",
    "\n",
    "mol_a = mol_dict['lig_ejm_43'] # query mol a rdkit\n",
    "mol_b = mol_dict['lig_ejm_48'] # query mol b rdkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "112a676d-f120-4007-a852-167dcfd7b7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this comes from `tests/test_topology.py`\n",
    "def validate_omm_BaseTopology_param_routine(mol, smirnoff_specs = (2, 1, 0), charge_spec = 'am1bcc'):\n",
    "    \"\"\"assert energy by energy match validation for a `mol` parameterized by\n",
    "    smirnoff x.y.z and `charge_spec` charges parameterized by `BaseTopology` \n",
    "    and another `mol` annotated with an `openmm.System` object parameterized consistently.\n",
    "    \"\"\"\n",
    "    # some of this boilerplate stuff is redundant. should reduce.\n",
    "    mol, off_omm_sys, tm_ff, _ = make_mol_omm_sys(mol, smirnoff_specs, charge_spec)\n",
    "    \n",
    "    conf = get_romol_conf(mol)\n",
    "\n",
    "    bt = BaseTopology(mol, tm_ff)\n",
    "    bt_sys = bt.setup_end_state()\n",
    "    annotate_mol_sys_torsions(mol, off_omm_sys, None, tm_ff)\n",
    "    mod_bt = BaseTopology(mol, tm_ff)\n",
    "    mod_bt_sys = mod_bt.setup_end_state()\n",
    "\n",
    "    assert np.isclose(bt_sys.bond(conf, None), mod_bt_sys.bond(conf, None))\n",
    "    assert np.isclose(bt_sys.angle(conf, None), mod_bt_sys.angle(conf, None))\n",
    "    assert np.isclose(bt_sys.torsion(conf, None), mod_bt_sys.torsion(conf, None))\n",
    "    assert np.isclose(bt_sys.nonbonded(conf, None), mod_bt_sys.nonbonded(conf, None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "009e8f87-cb35-4835-af34-41354e5629b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_omm_BaseTopology_param_routine(mol_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b01a4b46-5eac-45e7-9624-8df513108830",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_omm_BaseTopology_param_routine(mol_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0100686-271a-495a-b8a2-899a59e87afb",
   "metadata": {},
   "source": [
    "so there are no parameterization inconsistencies?\n",
    "perhaps off am1bcc charges are different from tm?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "010c3305-383b-4147-b2dd-aa9050f2ed81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# refresh mols because they may have been modified in place...\n",
    "mols = read_sdf(ligand_file) # read mols from sdf\n",
    "mol_dict = {get_mol_name(mol): mol for mol in mols} # make a mol dict\n",
    "\n",
    "mol_a = mol_dict['lig_ejm_43'] # query mol a rdkit\n",
    "mol_b = mol_dict['lig_ejm_48'] # query mol b rdkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1de321b5-990b-4b22-9fa7-3daa645f0600",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_tm_ff = Forcefield.load_from_file(f\"smirnoff_2_1_0_ccc.py\")\n",
    "bt = BaseTopology(mol_a, my_tm_ff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "84981249-1cfe-4532-8ba9-3d2e5873b17d",
   "metadata": {},
   "outputs": [],
   "source": [
    "endstate = bt.setup_end_state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "18530eab-0380-4daa-885c-5881c5fdfac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now make the mod system...\n",
    "charged_mol_a, off_omm_sys, mod_tm_ff, _ = make_mol_omm_sys(mol_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8f56bdcf-6a57-4280-8135-f9d4534d25b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_bt = BaseTopology(charged_mol_a, mod_tm_ff)\n",
    "mod_endstate = mod_bt.setup_end_state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0d6e0bad-c323-44c5-b355-b5d40c2620a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now compare nonbondeds..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2082a947-49b3-4ec3-ba25-169c6d817ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5150882 ,  0.30266142,  0.07704421,  0.        ],\n",
       "       [-2.79535961,  0.30266142,  0.15408842,  0.        ],\n",
       "       [ 0.5150882 ,  0.30266142,  0.07704421,  0.        ],\n",
       "       ...,\n",
       "       [-1.39063644,  0.29400545,  0.26941761,  0.        ],\n",
       "       [ 3.80331826,  0.25725815,  0.06531786,  0.        ],\n",
       "       [-0.63472831,  0.29400545,  0.13470881,  0.        ]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endstate.nonbonded.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "382b52ea-bffe-4a7a-849d-41273672804d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51382322,  0.30266142,  0.07704421,  0.        ],\n",
       "       [-2.90288493,  0.30266142,  0.15408842,  0.        ],\n",
       "       [ 0.51382322,  0.30266142,  0.07704421,  0.        ],\n",
       "       ...,\n",
       "       [-1.13172159,  0.29400545,  0.26941761,  0.        ],\n",
       "       [ 3.11410825,  0.25725815,  0.06531786,  0.        ],\n",
       "       [-0.62935875,  0.29400545,  0.13470881,  0.        ]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_endstate.nonbonded.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d38c378b-4917-404d-9606-21bdfe3df5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# so the precomputed charges are different...that might explain the difference..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f24d1137-7150-4eb5-a67a-f0f2821f9d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for reference/posterity, lets print the collected data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6ce8283f-7859-4440-9492-e05ba3ce05a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(9, 6): {'comp': array([12.44502868,  0.07648184]),\n",
       "  'solv': array([10.69072658,  0.06692161]),\n",
       "  'total': array([1.7543021 , 0.10038241]),\n",
       "  'exp': array([0.20558054, 0.25292981])},\n",
       " (10, 6): {'comp': array([41.0707457 ,  0.05975143]),\n",
       "  'solv': array([38.34608031,  0.05497132]),\n",
       "  'total': array([2.72227533, 0.08126195]),\n",
       "  'exp': array([2.72108765, 0.25292981])},\n",
       " (12, 6): {'comp': array([29.35946463,  0.06453155]),\n",
       "  'solv': array([27.62428298,  0.05736138]),\n",
       "  'total': array([1.73518164, 0.08604207]),\n",
       "  'exp': array([1.99152012, 0.25292981])},\n",
       " (7, 6): {'comp': array([16.29063098,  0.07409178]),\n",
       "  'solv': array([15.2748566 ,  0.06453155]),\n",
       "  'total': array([1.01577438, 0.09799235]),\n",
       "  'exp': array([-0.02433649,  0.25292981])},\n",
       " (2, 6): {'comp': array([9.84703633, 0.08126195]),\n",
       "  'solv': array([9.65344168, 0.06931166]),\n",
       "  'total': array([0.19120459, 0.10755258]),\n",
       "  'exp': array([-0.74684877,  0.25292981])},\n",
       " (0, 6): {'comp': array([11.50095602,  0.06692161]),\n",
       "  'solv': array([10.83652008,  0.05736138]),\n",
       "  'total': array([0.66443595, 0.08843212]),\n",
       "  'exp': array([0.54625705, 0.25292981])},\n",
       " (8, 6): {'comp': array([9.05353728, 0.08365201]),\n",
       "  'solv': array([8.07122371, 0.06931166]),\n",
       "  'total': array([0.98231358, 0.10994264]),\n",
       "  'exp': array([1.54421698, 0.25292981])},\n",
       " (4, 6): {'comp': array([40.55210325,  0.05736138]),\n",
       "  'solv': array([38.69502868,  0.05258126]),\n",
       "  'total': array([1.85707457, 0.07887189]),\n",
       "  'exp': array([2.33219663, 0.25292981])},\n",
       " (3, 6): {'comp': array([21.19024857,  0.08843212]),\n",
       "  'solv': array([19.50525813,  0.07409178]),\n",
       "  'total': array([1.68499044, 0.11472275]),\n",
       "  'exp': array([0.55880828, 0.25292981])},\n",
       " (11, 6): {'comp': array([39.97848948,  0.06453155]),\n",
       "  'solv': array([37.40200765,  0.05736138]),\n",
       "  'total': array([2.57648184, 0.08604207]),\n",
       "  'exp': array([2.29605458, 0.25292981])},\n",
       " (1, 6): {'comp': array([13.36759082,  0.07887189]),\n",
       "  'solv': array([13.19072658,  0.06214149]),\n",
       "  'total': array([0.17686424, 0.10038241]),\n",
       "  'exp': array([0.78797965, 0.25292981])},\n",
       " (5, 6): {'comp': array([-1.09703633,  0.0501912 ]),\n",
       "  'solv': array([-1.80688337,  0.0501912 ]),\n",
       "  'total': array([0.71223709, 0.07170172]),\n",
       "  'exp': array([0.70142776, 0.25292981])}}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_annotated_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d5d2bf43-957d-402c-b144-219328a916d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(11, 6): {'comp': array([41.00860421,  0.06453155]),\n",
       "  'solv': array([38.94837476,  0.05736138]),\n",
       "  'total': array([2.06022945, 0.08604207]),\n",
       "  'exp': array([2.29605458, 0.25292981])},\n",
       " (12, 6): {'comp': array([30.04063098,  0.06692161]),\n",
       "  'solv': array([28.83365201,  0.05736138]),\n",
       "  'total': array([1.20697897, 0.08843212]),\n",
       "  'exp': array([1.99152012, 0.25292981])},\n",
       " (1, 6): {'comp': array([13.6376673 ,  0.07648184]),\n",
       "  'solv': array([12.74378585,  0.06453155]),\n",
       "  'total': array([0.8914914 , 0.10038241]),\n",
       "  'exp': array([0.78797965, 0.25292981])},\n",
       " (10, 6): {'comp': array([41.89770554,  0.06214149]),\n",
       "  'solv': array([39.56022945,  0.05258126]),\n",
       "  'total': array([2.3374761 , 0.08126195]),\n",
       "  'exp': array([2.72108765, 0.25292981])},\n",
       " (7, 6): {'comp': array([18.81692161,  0.07648184]),\n",
       "  'solv': array([17.3876673 ,  0.06453155]),\n",
       "  'total': array([1.4292543 , 0.10038241]),\n",
       "  'exp': array([-0.02433649,  0.25292981])},\n",
       " (4, 6): {'comp': array([40.09799235,  0.05736138]),\n",
       "  'solv': array([38.35564054,  0.05258126]),\n",
       "  'total': array([1.74235182, 0.07648184]),\n",
       "  'exp': array([2.33219663, 0.25292981])},\n",
       " (9, 6): {'comp': array([14.95697897,  0.07648184]),\n",
       "  'solv': array([12.78202677,  0.06692161]),\n",
       "  'total': array([2.1749522 , 0.10038241]),\n",
       "  'exp': array([0.20558054, 0.25292981])},\n",
       " (3, 6): {'comp': array([19.93068834,  0.09082218]),\n",
       "  'solv': array([19.41443595,  0.06931166]),\n",
       "  'total': array([0.51625239, 0.11472275]),\n",
       "  'exp': array([0.55880828, 0.25292981])},\n",
       " (5, 6): {'comp': array([0.71223709, 0.0501912 ]),\n",
       "  'solv': array([0.05975143, 0.04780115]),\n",
       "  'total': array([0.65248566, 0.06931166]),\n",
       "  'exp': array([0.70142776, 0.25292981])},\n",
       " (8, 6): {'comp': array([11.2332696 ,  0.08126195]),\n",
       "  'solv': array([9.27581262, 0.07170172]),\n",
       "  'total': array([1.95745698, 0.10755258]),\n",
       "  'exp': array([1.54421698, 0.25292981])},\n",
       " (0, 6): {'comp': array([13.51816444,  0.06931166]),\n",
       "  'solv': array([12.51195029,  0.05736138]),\n",
       "  'total': array([1.00621415, 0.09082218]),\n",
       "  'exp': array([0.54625705, 0.25292981])},\n",
       " (2, 6): {'comp': array([10.40630975,  0.08365201]),\n",
       "  'solv': array([11.81166348,  0.06692161]),\n",
       "  'total': array([-1.40296367,  0.10755258]),\n",
       "  'exp': array([-0.74684877,  0.25292981])}}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "off_annotated_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047ebdf4-e4fd-4939-8311-2103449702fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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