tm_jak2_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ba33168-3d63-4dc2-904e-f6993b75d37a",
   "metadata": {},
   "source": [
    "analysis of jak2 with/without modified timemachine..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 scipy\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": 2,
   "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": 25,
   "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",
    "                              do_linregress: bool=True):\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",
    "    if do_linregress:\n",
    "        res = scipy.stats.linregress(calc_dGs, exp_dGs)\n",
    "        fit_y = res.slope * x_ref + res.intercept\n",
    "        r2 = res.rvalue**2\n",
    "        plt.plot(x_ref, fit_y, label=f\"best fit line: R^2 = {r2}\")\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": 26,
   "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": 8,
   "id": "6d4e512c-f20c-471e-adfb-3c377a43604f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/choderaj/rufad/ipykernel_939520/3742238339.py:162: UserWarning: No SDtag 'pChEMBL Error' for pIC50 error found, fixing to default value of 0.3\n",
      "  warnings.warn(f\"No SDtag '{pic50_error_sdtag}' for pIC50 error found, fixing to default value of 0.3\")\n"
     ]
    }
   ],
   "source": [
    "dir_to_query = '/data1/choderaj/rufad/tm/jak2_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_exp_data = get_experimental_estimates(sdfile = os.path.join(dir_to_query, \n",
    "    'ligands_from_FEP_cations_SUBSET_IC50_uM_largercore.sdf'))\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": 10,
   "id": "a4d84c78-0e16-466b-b16d-d9edd84fb0e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# it looks like there was a failed edge in the calculation...lets load it...\n",
    "import pickle\n",
    "failure_pickle = 'failure_rbfe_result_CHEMBL3642389_CHEMBL3645060.pkl'\n",
    "with open(os.path.join(dir_to_query,failure_pickle), 'rb') as f:\n",
    "    loaded_object = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea98cb6e-116a-446c-995f-d28a9379dca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Edge(mol_a_name='CHEMBL3642389', mol_b_name='CHEMBL3645060', metadata={}),\n",
       " timemachine.md.minimizer.MinimizationError('\\n        Minimization failed to reduce large forces below threshold:\\n            max |frc| = 52172.602132876156 > 50000.0\\n            1 / 279 atoms exceed threshold\\n        '),\n",
       " ['Traceback (most recent call last):\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 936, in run_edge_and_save_results\\n    solvent_res, solvent_top, _ = run_solvent(\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 843, in run_solvent\\n    solvent_res = estimate_relative_free_energy_bisection_or_hrex(\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 472, in estimate_relative_free_energy_bisection_or_hrex\\n    return estimate_fxn(*args, **kwargs)\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 771, in estimate_relative_free_energy_bisection_hrex\\n    initial_states = setup_initial_states(\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 232, in setup_initial_states\\n    optimized_x0s = optimize_coordinates(initial_states, min_cutoff=min_cutoff)\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 355, in optimize_coordinates\\n    rhs_xs = _optimize_coords_along_states(rhs_initial_states[::-1])[::-1]\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 307, in _optimize_coords_along_states\\n    x_opt = optimize_coords_state(\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/fe/rbfe.py\", line 284, in optimize_coords_state\\n    x_opt = minimizer.local_minimize(x0, val_and_grad_fn, free_idxs, assert_energy_decreased=assert_energy_decreased)\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/md/minimizer.py\", line 544, in local_minimize\\n    check_force_norm(forces)\\n',\n",
       "  '  File \"/home/rufad/github/timemachine/timemachine/md/minimizer.py\", line 43, in check_force_norm\\n    raise MinimizationError(message)\\n',\n",
       "  'timemachine.md.minimizer.MinimizationError: \\n        Minimization failed to reduce large forces below threshold:\\n            max |frc| = 52172.602132876156 > 50000.0\\n            1 / 279 atoms exceed threshold\\n        \\n'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b44f87eb-2c14-4bc3-b12e-b383c0772472",
   "metadata": {},
   "outputs": [],
   "source": [
    "# looks like a failed minimization..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a4dd86c-9edb-4782-8883-c6ecb811308a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/choderaj/rufad/ipykernel_939520/3742238339.py:162: UserWarning: No SDtag 'pChEMBL Error' for pIC50 error found, fixing to default value of 0.3\n",
      "  warnings.warn(f\"No SDtag '{pic50_error_sdtag}' for pIC50 error found, fixing to default value of 0.3\")\n"
     ]
    }
   ],
   "source": [
    "off_dir_to_query = '/data1/choderaj/rufad/tm/jak2_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_exp_data = get_experimental_estimates(sdfile = os.path.join(off_dir_to_query, \n",
    "    'ligands_from_FEP_cations_SUBSET_IC50_uM_largercore.sdf'))\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": 32,
   "id": "9f963a63-37ca-462c-8bc8-b819b4a97d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# it looks like there was a failed edge in the calculation...lets load it...\n",
    "import pickle\n",
    "# looks like this failed in complex phase...\n",
    "failure_pickle = 'failure_rbfe_result_CHEMBL3645071_CHEMBL3645074.pkl'\n",
    "with open(os.path.join(off_dir_to_query,failure_pickle), 'rb') as f:\n",
    "    loaded_object = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "265a5527-0258-4288-9a1b-c012c450c90e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Edge(mol_a_name='CHEMBL3645071', mol_b_name='CHEMBL3645074', metadata={}),\n",
       " numpy.linalg.LinAlgError('SVD did not converge in Linear Least Squares'),\n",
       " ['Traceback (most recent call last):\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/rbfe.py\", line 928, in run_edge_and_save_results\\n    complex_res, complex_top, _ = run_complex(\\n                                  ^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/rbfe.py\", line 874, in run_complex\\n    complex_res = estimate_relative_free_energy_bisection_or_hrex(\\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/rbfe.py\", line 472, in estimate_relative_free_energy_bisection_or_hrex\\n    return estimate_fxn(*args, **kwargs)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/rbfe.py\", line 787, in estimate_relative_free_energy_bisection_hrex\\n    return estimate_relative_free_energy_bisection_hrex_impl(\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/rbfe.py\", line 693, in estimate_relative_free_energy_bisection_hrex_impl\\n    raise err\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/rbfe.py\", line 666, in estimate_relative_free_energy_bisection_hrex_impl\\n    pair_bar_result, trajectories_by_state, diagnostics = run_sims_hrex(\\n                                                          ^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/free_energy.py\", line 1069, in run_sims_hrex\\n    bar_results = list(\\n                  ^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/utils.py\", line 32, in pairwise_transform_and_combine\\n    yield g(prev_b, b)\\n          ^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/free_energy.py\", line 1073, in <lambda>\\n    lambda s1, s2: estimate_free_energy_bar(compute_energy_decomposed_u_kln([s1, s2]), temperature),\\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/free_energy.py\", line 561, in estimate_free_energy_bar\\n    df, df_err = bar_with_bootstrapped_uncertainty(u_kln)  # reduced units\\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/bar.py\", line 207, in bar_with_bootstrapped_uncertainty\\n    df, bootstrap_dfs = bootstrap_bar(u_kln, n_bootstrap=n_bootstrap, maximum_iterations=maximum_iterations)\\n                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/github2/timemachine/timemachine/fe/bar.py\", line 179, in bootstrap_bar\\n    mbar = pymbar.MBAR(u_kn, N_k, maximum_iterations=maximum_iterations)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/pymbar/mbar.py\", line 312, in __init__\\n    self.f_k = mbar_solvers.solve_mbar_for_all_states(self.u_kn, self.N_k, self.f_k, solver_protocol, solver_tolerance)\\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/pymbar/mbar_solvers.py\", line 558, in solve_mbar_for_all_states\\n    f_k_nonzero, all_results = solve_mbar(u_kn[states_with_samples], N_k[states_with_samples],\\n                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/pymbar/mbar_solvers.py\", line 524, in solve_mbar\\n    f_k_nonzero, results = solve_mbar_once(u_kn_nonzero, N_k_nonzero, f_k_nonzero, tol=solver_tolerance, **options)\\n                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/pymbar/mbar_solvers.py\", line 451, in solve_mbar_once\\n    results = adaptive(u_kn_nonzero, N_k_nonzero, f_k_nonzero, tol=tol, options=options)\\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/pymbar/mbar_solvers.py\", line 300, in adaptive\\n    Hinvg = np.linalg.lstsq(H, g, rcond=-1)[0]\\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/numpy/linalg/linalg.py\", line 2326, in lstsq\\n    x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)\\n                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n',\n",
       "  '  File \"/home/rufad/micromamba/envs/timemachine_off/lib/python3.11/site-packages/numpy/linalg/linalg.py\", line 124, in _raise_linalgerror_lstsq\\n    raise LinAlgError(\"SVD did not converge in Linear Least Squares\")\\n',\n",
       "  'numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares\\n'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845f630f-283f-4cd6-9b1e-83b27b93c865",
   "metadata": {},
   "source": [
    "so mbar failed?! weird..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d72ed66c-8c41-45df-85f1-97ee021beb75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/choderaj/rufad/ipykernel_939520/3742238339.py:162: UserWarning: No SDtag 'pChEMBL Error' for pIC50 error found, fixing to default value of 0.3\n",
      "  warnings.warn(f\"No SDtag '{pic50_error_sdtag}' for pIC50 error found, fixing to default value of 0.3\")\n"
     ]
    }
   ],
   "source": [
    "esp_dir_to_query = '/data1/choderaj/rufad/tm/jak2_esp1/'\n",
    "esp_data_dict = build_data_dict_from_outs(esp_dir_to_query)\n",
    "#off_exp_data = load_exp_data_from_yml('tyk2_ligands.yml')\n",
    "esp_exp_data = get_experimental_estimates(sdfile = os.path.join(esp_dir_to_query, \n",
    "    'ligands_from_FEP_cations_SUBSET_IC50_uM_largercore.sdf'))\n",
    "esp_annotated_data = annotate_data_dict_w_exp(esp_data_dict, esp_exp_data, rewrite_ligands_to_idxs=True)\n",
    "esp_my_inputs = node_vals_and_errs_inputs_from_my_data(esp_exp_data, esp_annotated_data)\n",
    "esp_dgs, esp_dg_errs = infer_node_vals_and_errs(*esp_my_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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: jak2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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: jak2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ec21f65c-89ed-42af-a13a-34797f073ef0",
   "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(esp_dgs, esp_my_inputs[4], esp_dg_errs, esp_my_inputs[-1], title = f\"modified tm: jak2-esp=0.3.2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "de7bdb77-3392-4b4a-b9e6-b289edbc04f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is wrong...I failed to disengage the `use_espaloma` flag for `modified tm: jak2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "da2f88de-5cae-463e-a466-c92c5590aa60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 25)\n"
     ]
    }
   ],
   "source": [
    "ddG_data = []\n",
    "for key, val in orig_annotated_data.items():\n",
    "    orig_data = val['total']\n",
    "    try:\n",
    "        off_data = off_annotated_data[key]['total']\n",
    "    except Exception as e:\n",
    "        print(f\"{e}\")\n",
    "        continue\n",
    "    ddG_data.append(np.concatenate([orig_data, off_data]))\n",
    "ddG_data = np.array(ddG_data)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "95b440a3-44c3-484c-a97d-a4405f745fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# there is one that fails on account of inconsistent failures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "id": "dde842fb-4dbc-4037-a623-ba9e2de0a6e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now must do a few esp repeats and possibly extend to 2-5ns/replica (though that may be prohibitive...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c69ff1a4-aad9-483a-9dab-7a5352e15766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(32, 38): {'comp': array([-19.50286807,   0.08843212]),\n",
       "  'solv': array([-18.59942639,   0.08604207]),\n",
       "  'total': array([-0.90344168,  0.12189293]),\n",
       "  'exp': array([0.16472531, 0.42426407])},\n",
       " (0, 5): {'comp': array([0.23422562, 0.03824092]),\n",
       "  'solv': array([-1.11137667,  0.03585086]),\n",
       "  'total': array([1.34321224, 0.05258126]),\n",
       "  'exp': array([0.27454214, 0.42426407])},\n",
       " (38, 20): {'comp': array([-6.65152964,  0.08843212]),\n",
       "  'solv': array([-9.00334608,  0.08365201]),\n",
       "  'total': array([2.34942639, 0.12189293]),\n",
       "  'exp': array([1.59234641, 0.42426407])},\n",
       " (7, 3): {'comp': array([-8.03298279,  0.02629063]),\n",
       "  'solv': array([-6.70172084,  0.02629063]),\n",
       "  'total': array([-1.33126195,  0.03824092]),\n",
       "  'exp': array([-2.53951719,  0.42426407])},\n",
       " (24, 6): {'comp': array([-10.34894837,   0.07887189]),\n",
       "  'solv': array([-8.54684512,  0.06931166]),\n",
       "  'total': array([-1.79971319,  0.10516252]),\n",
       "  'exp': array([0.16472531, 0.42426407])},\n",
       " (15, 24): {'comp': array([3.45602294, 0.06214149]),\n",
       "  'solv': array([4.91156788, 0.05497132]),\n",
       "  'total': array([-1.45554493,  0.08126195]),\n",
       "  'exp': array([0.86480874, 0.42426407])},\n",
       " (32, 41): {'comp': array([-14.94024857,   0.09082218]),\n",
       "  'solv': array([-14.81118547,   0.08843212]),\n",
       "  'total': array([-0.1290631,  0.1290631]),\n",
       "  'exp': array([0.54908497, 0.42426407])},\n",
       " (13, 27): {'comp': array([-14.29254302,   0.08126195]),\n",
       "  'solv': array([-13.41061185,   0.07170172]),\n",
       "  'total': array([-0.88193117,  0.10994264]),\n",
       "  'exp': array([-1.11189664,  0.42426407])},\n",
       " (35, 39): {'comp': array([9.54110899, 0.08126195]),\n",
       "  'solv': array([9.55066922, 0.07887189]),\n",
       "  'total': array([-0.00956023,  0.11472275]),\n",
       "  'exp': array([0.98835297, 0.42426407])},\n",
       " (12, 1): {'comp': array([10.50669216,  0.04780115]),\n",
       "  'solv': array([9.68690249, 0.04302103]),\n",
       "  'total': array([0.81978967, 0.06214149]),\n",
       "  'exp': array([-0.57653921,  0.42426407])},\n",
       " (38, 2): {'comp': array([27.16061185,  0.10994264]),\n",
       "  'solv': array([27.39722753,  0.10277247]),\n",
       "  'total': array([-0.23661568,  0.15057361]),\n",
       "  'exp': array([1.11189664, 0.42426407])},\n",
       " (8, 5): {'comp': array([-1.60133843,  0.0334608 ]),\n",
       "  'solv': array([-1.94789675,  0.03585086]),\n",
       "  'total': array([0.34655832, 0.0501912 ]),\n",
       "  'exp': array([-0.13727107,  0.42426407])},\n",
       " (15, 6): {'comp': array([-6.1998088 ,  0.06453155]),\n",
       "  'solv': array([-1.59655832,  0.06214149]),\n",
       "  'total': array([-4.60325048,  0.09082218]),\n",
       "  'exp': array([1.02953405, 0.42426407])},\n",
       " (22, 28): {'comp': array([-11.35994264,   0.12189293]),\n",
       "  'solv': array([-8.0043021 ,  0.11950287]),\n",
       "  'total': array([-3.35564054,  0.16969407]),\n",
       "  'exp': array([-1.12562472,  0.42426407])},\n",
       " (23, 29): {'comp': array([2.38288719, 0.09321224]),\n",
       "  'solv': array([2.93499044, 0.08843212]),\n",
       "  'total': array([-0.55210325,  0.1290631 ]),\n",
       "  'exp': array([-0.65890248,  0.42426407])},\n",
       " (38, 11): {'comp': array([16.30975143,  0.10516252]),\n",
       "  'solv': array([17.58365201,  0.10277247]),\n",
       "  'total': array([-1.27390057,  0.1457935 ]),\n",
       "  'exp': array([0.35690541, 0.42426407])},\n",
       " (46, 42): {'comp': array([-3.43929254,  0.05736138]),\n",
       "  'solv': array([-2.86089866,  0.05497132]),\n",
       "  'total': array([-0.57839388,  0.07887189]),\n",
       "  'exp': array([-1.49625629,  0.42426407])},\n",
       " (15, 3): {'comp': array([-0.82217973,  0.08365201]),\n",
       "  'solv': array([1.83556405, 0.08126195]),\n",
       "  'total': array([-2.66013384,  0.11711281]),\n",
       "  'exp': array([1.0432609 , 0.42426407])},\n",
       " (30, 41): {'comp': array([-14.65344168,   0.10755258]),\n",
       "  'solv': array([-14.62715105,   0.10038241]),\n",
       "  'total': array([-0.02868069,  0.14818356]),\n",
       "  'exp': array([0.04118164, 0.42426407])},\n",
       " (34, 39): {'comp': array([-12.6290631 ,   0.05497132]),\n",
       "  'solv': array([-10.94407266,   0.05258126]),\n",
       "  'total': array([-1.68499044,  0.07648184]),\n",
       "  'exp': array([-0.98835242,  0.42426407])},\n",
       " (31, 4): {'comp': array([12.06739962,  0.06214149]),\n",
       "  'solv': array([10.42304015,  0.05736138]),\n",
       "  'total': array([1.64435946, 0.08365201]),\n",
       "  'exp': array([1.13935088, 0.42426407])},\n",
       " (15, 1): {'comp': array([-6.94550669,  0.07648184]),\n",
       "  'solv': array([-5.13623327,  0.06931166]),\n",
       "  'total': array([-1.80688337,  0.10277247]),\n",
       "  'exp': array([0.46672169, 0.42426407])},\n",
       " (26, 45): {'comp': array([15.98470363,  0.10038241]),\n",
       "  'solv': array([18.76195029,  0.09082218]),\n",
       "  'total': array([-2.77724665,  0.13623327]),\n",
       "  'exp': array([0.52162935, 0.42426407])},\n",
       " (34, 25): {'comp': array([-6.71128107,  0.05258126]),\n",
       "  'solv': array([-5.10755258,  0.0501912 ]),\n",
       "  'total': array([-1.60611855,  0.07409178]),\n",
       "  'exp': array([-0.71381028,  0.42426407])},\n",
       " (36, 41): {'comp': array([-17.58843212,   0.10038241]),\n",
       "  'solv': array([-16.90248566,   0.09560229]),\n",
       "  'total': array([-0.68833652,  0.13862333]),\n",
       "  'exp': array([-0.41181321,  0.42426407])},\n",
       " (37, 39): {'comp': array([-8.53728489,  0.05497132]),\n",
       "  'solv': array([-9.61520076,  0.05497132]),\n",
       "  'total': array([1.07791587, 0.07887189]),\n",
       "  'exp': array([-1.4550748 ,  0.42426407])},\n",
       " (10, 16): {'comp': array([-0.45172084,  0.04541109]),\n",
       "  'solv': array([1.00382409, 0.04063098]),\n",
       "  'total': array([-1.45793499,  0.05975143]),\n",
       "  'exp': array([-1.08444254,  0.42426407])},\n",
       " (38, 33): {'comp': array([7.65774379, 0.09799235]),\n",
       "  'solv': array([8.81453155, 0.09321224]),\n",
       "  'total': array([-1.15678776,  0.13623327]),\n",
       "  'exp': array([0.        , 0.42426407])},\n",
       " (7, 9): {'comp': array([-0.59512428,  0.05736138]),\n",
       "  'solv': array([0.65726577, 0.05736138]),\n",
       "  'total': array([-1.25239006,  0.08126195]),\n",
       "  'exp': array([-0.631447  ,  0.42426407])},\n",
       " (14, 28): {'comp': array([-2.94694073,  0.05497132]),\n",
       "  'solv': array([-3.87667304,  0.05258126]),\n",
       "  'total': array([0.93212237, 0.07648184]),\n",
       "  'exp': array([0.19217887, 0.42426407])},\n",
       " (15, 43): {'comp': array([-14.12523901,   0.08126195]),\n",
       "  'solv': array([-12.42351816,   0.07887189]),\n",
       "  'total': array([-1.70172084,  0.1123327 ]),\n",
       "  'exp': array([-0.08236327,  0.42426407])},\n",
       " (3, 9): {'comp': array([7.87045889, 0.0501912 ]),\n",
       "  'solv': array([8.27437859, 0.04780115]),\n",
       "  'total': array([-0.40391969,  0.06931166]),\n",
       "  'exp': array([1.90807019, 0.42426407])},\n",
       " (31, 21): {'comp': array([7.94694073, 0.04302103]),\n",
       "  'solv': array([5.93690249, 0.04063098]),\n",
       "  'total': array([2.00764818, 0.05975143]),\n",
       "  'exp': array([0.85108135, 0.42426407])},\n",
       " (17, 31): {'comp': array([5.65726577, 0.08126195]),\n",
       "  'solv': array([7.04110899, 0.07409178]),\n",
       "  'total': array([-1.38145315,  0.1123327 ]),\n",
       "  'exp': array([-0.61772016,  0.42426407])},\n",
       " (29, 28): {'comp': array([-1.33365201,  0.08604207]),\n",
       "  'solv': array([-1.54636711,  0.08365201]),\n",
       "  'total': array([0.21271511, 0.12189293]),\n",
       "  'exp': array([0.17845271, 0.42426407])},\n",
       " (4, 21): {'comp': array([-3.88862333,  0.04302103]),\n",
       "  'solv': array([-4.45267686,  0.03824092]),\n",
       "  'total': array([0.56405354, 0.05736138]),\n",
       "  'exp': array([-0.28826953,  0.42426407])},\n",
       " (10, 0): {'comp': array([-1.2165392 ,  0.04780115]),\n",
       "  'solv': array([-0.92256214,  0.04780115]),\n",
       "  'total': array([-0.29397706,  0.06931166]),\n",
       "  'exp': array([-1.11189678,  0.42426407])},\n",
       " (16, 8): {'comp': array([1.11376673, 0.02868069]),\n",
       "  'solv': array([-0.42304015,  0.02629063]),\n",
       "  'total': array([1.53680688, 0.03824092]),\n",
       "  'exp': array([0.38435897, 0.42426407])},\n",
       " (30, 38): {'comp': array([-19.92351816,   0.10038241]),\n",
       "  'solv': array([-18.09512428,   0.09560229]),\n",
       "  'total': array([-1.83078394,  0.13862333]),\n",
       "  'exp': array([-0.34317802,  0.42426407])},\n",
       " (45, 42): {'comp': array([8.60898662, 0.05258126]),\n",
       "  'solv': array([7.53585086, 0.05258126]),\n",
       "  'total': array([1.0707457 , 0.07409178]),\n",
       "  'exp': array([0.35690555, 0.42426407])},\n",
       " (42, 29): {'comp': array([-39.40726577,   0.09799235]),\n",
       "  'solv': array([-38.47992352,   0.09799235]),\n",
       "  'total': array([-0.92973231,  0.13862333]),\n",
       "  'exp': array([-0.86480874,  0.42426407])},\n",
       " (16, 38): {'comp': array([-15.10994264,   0.06692161]),\n",
       "  'solv': array([-12.95172084,   0.06453155]),\n",
       "  'total': array([-2.1582218 ,  0.09321224]),\n",
       "  'exp': array([-0.70008343,  0.42426407])},\n",
       " (19, 23): {'comp': array([1.21414914, 0.10516252]),\n",
       "  'solv': array([1.79732314, 0.09560229]),\n",
       "  'total': array([-0.58078394,  0.14340344]),\n",
       "  'exp': array([0.76871945, 0.42426407])},\n",
       " (41, 37): {'comp': array([-3.70936902,  0.05736138]),\n",
       "  'solv': array([-3.14531549,  0.05497132]),\n",
       "  'total': array([-0.56405354,  0.07887189]),\n",
       "  'exp': array([-0.10981683,  0.42426407])},\n",
       " (0, 3): {'comp': array([-4.7418738 ,  0.06931166]),\n",
       "  'solv': array([-1.80927342,  0.06453155]),\n",
       "  'total': array([-2.93260038,  0.09560229]),\n",
       "  'exp': array([-0.64517495,  0.42426407])},\n",
       " (32, 18): {'comp': array([-2.12237094,  0.12667304]),\n",
       "  'solv': array([-4.27581262,  0.1123327 ]),\n",
       "  'total': array([2.15105163, 0.16969407]),\n",
       "  'exp': array([0.32945062, 0.42426407])},\n",
       " (46, 17): {'comp': array([-9.50047801,  0.10277247]),\n",
       "  'solv': array([-10.29636711,   0.10038241]),\n",
       "  'total': array([0.7958891 , 0.14340344]),\n",
       "  'exp': array([0.74126452, 0.42426407])},\n",
       " (6, 5): {'comp': array([8.29349904, 0.05497132]),\n",
       "  'solv': array([4.6582218 , 0.05258126]),\n",
       "  'total': array([3.6376673 , 0.07648184]),\n",
       "  'exp': array([0.93344393, 0.42426407])},\n",
       " (26, 42): {'comp': array([25.38479924,  0.06453155]),\n",
       "  'solv': array([26.14961759,  0.06214149]),\n",
       "  'total': array([-0.7624283 ,  0.08843212]),\n",
       "  'exp': array([0.8785349 , 0.42426407])},\n",
       " (17, 13): {'comp': array([13.47036329,  0.07170172]),\n",
       "  'solv': array([12.17017208,  0.06214149]),\n",
       "  'total': array([1.3001912 , 0.09560229]),\n",
       "  'exp': array([0.28826953, 0.42426407])},\n",
       " (43, 34): {'comp': array([10.91778203,  0.08365201]),\n",
       "  'solv': array([10.17447419,  0.08126195]),\n",
       "  'total': array([0.74330784, 0.11711281]),\n",
       "  'exp': array([0.90599038, 0.42426407])},\n",
       " (40, 0): {'comp': array([-0.12428298,  0.13623327]),\n",
       "  'solv': array([-0.95363289,  0.12667304]),\n",
       "  'total': array([0.8293499 , 0.18642447]),\n",
       "  'exp': array([1.27662264, 0.42426407])},\n",
       " (39, 25): {'comp': array([3.88384321, 0.0501912 ]),\n",
       "  'solv': array([3.17160612, 0.0501912 ]),\n",
       "  'total': array([0.71223709, 0.07170172]),\n",
       "  'exp': array([0.27454214, 0.42426407])},\n",
       " (19, 29): {'comp': array([5.20076482, 0.09799235]),\n",
       "  'solv': array([5.09560229, 0.09082218]),\n",
       "  'total': array([0.10516252, 0.13384321]),\n",
       "  'exp': array([0.10981697, 0.42426407])},\n",
       " (27, 31): {'comp': array([7.35898662, 0.09321224]),\n",
       "  'solv': array([7.6792543 , 0.08604207]),\n",
       "  'total': array([-0.31787763,  0.12667304]),\n",
       "  'exp': array([0.20590695, 0.42426407])},\n",
       " (12, 6): {'comp': array([-8.49665392,  0.06931166]),\n",
       "  'solv': array([-7.63145315,  0.06214149]),\n",
       "  'total': array([-0.86520076,  0.09321224]),\n",
       "  'exp': array([-0.01372685,  0.42426407])},\n",
       " (44, 15): {'comp': array([9.63193117, 0.12189293]),\n",
       "  'solv': array([10.76481836,  0.12667304]),\n",
       "  'total': array([-1.13527725,  0.17447419]),\n",
       "  'exp': array([-0.53535757,  0.42426407])},\n",
       " (18, 38): {'comp': array([-26.52724665,   0.1123327 ]),\n",
       "  'solv': array([-22.61950287,   0.10516252]),\n",
       "  'total': array([-3.90774379,  0.15535373]),\n",
       "  'exp': array([-0.16472531,  0.42426407])},\n",
       " (17, 27): {'comp': array([-1.68021033,  0.03824092]),\n",
       "  'solv': array([-1.09703633,  0.03824092]),\n",
       "  'total': array([-0.58078394,  0.05497132]),\n",
       "  'exp': array([-0.82362711,  0.42426407])},\n",
       " (41, 33): {'comp': array([5.48518164, 0.10755258]),\n",
       "  'solv': array([7.87284895, 0.09560229]),\n",
       "  'total': array([-2.3876673 ,  0.14340344]),\n",
       "  'exp': array([-0.38435965,  0.42426407])},\n",
       " (35, 37): {'comp': array([18.56596558,  0.07409178]),\n",
       "  'solv': array([19.24474187,  0.07409178]),\n",
       "  'total': array([-0.67877629,  0.10516252]),\n",
       "  'exp': array([2.44342776, 0.42426407])},\n",
       " (22, 43): {'comp': array([-1.71128107,  0.1123327 ]),\n",
       "  'solv': array([-3.24091778,  0.10755258]),\n",
       "  'total': array([1.53202677, 0.15535373]),\n",
       "  'exp': array([0.01372616, 0.42426407])},\n",
       " (14, 11): {'comp': array([27.43068834,  0.12189293]),\n",
       "  'solv': array([27.29445507,  0.1123327 ]),\n",
       "  'total': array([0.13623327, 0.16491396]),\n",
       "  'exp': array([2.78660509, 0.42426407])},\n",
       " (36, 38): {'comp': array([-21.75908222,   0.09799235]),\n",
       "  'solv': array([-20.51386233,   0.09321224]),\n",
       "  'total': array([-1.24521989,  0.13384321]),\n",
       "  'exp': array([-0.79617286,  0.42426407])},\n",
       " (20, 2): {'comp': array([33.34608031,  0.09082218]),\n",
       "  'solv': array([34.9665392 ,  0.08604207]),\n",
       "  'total': array([-1.62284895,  0.12428298]),\n",
       "  'exp': array([-0.48044977,  0.42426407])},\n",
       " (40, 38): {'comp': array([-8.18833652,  0.13145315]),\n",
       "  'solv': array([-6.73040153,  0.12428298]),\n",
       "  'total': array([-1.45793499,  0.1792543 ]),\n",
       "  'exp': array([0.60399345, 0.42426407])},\n",
       " (26, 46): {'comp': array([28.41300191,  0.07648184]),\n",
       "  'solv': array([29.0583174 ,  0.06931166]),\n",
       "  'total': array([-0.64531549,  0.10277247]),\n",
       "  'exp': array([2.3747912 , 0.42426407])},\n",
       " (27, 0): {'comp': array([5.33938815, 0.10516252]),\n",
       "  'solv': array([2.61711281, 0.10038241]),\n",
       "  'total': array([2.71988528, 0.1457935 ]),\n",
       "  'exp': array([0.80990026, 0.42426407])},\n",
       " (44, 43): {'comp': array([-13.38193117,   0.1123327 ]),\n",
       "  'solv': array([-11.49856597,   0.11472275]),\n",
       "  'total': array([-1.8833652 ,  0.16013384]),\n",
       "  'exp': array([-0.61772084,  0.42426407])}}"
      ]
     },
     "execution_count": 45,
     "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": {
  "kernelspec": {
   "display_name": "timemachine_off",
   "language": "python",
   "name": "timemachine_off"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

tm_tyk2_analysis.ipynb

comparing my modified timemachine (w/ openff support, but specifically off2.1.0) for tyk2 and jak2.