TorchANI_2D.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/pablo-arantes/ParametrizANI/blob/main/TorchANI_2D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pj2BWZxUDbEE"
      },
      "source": [
        "# **ParametrizANI - Dihedral Parametrization in the Cloud with TorchANI**\n",
        "\n",
        "This is a Jupyter notebook to set up a protocol to calculate a reference map for two dihedrals angles with TorchANI , a PyTorch-based program for training/inference of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical properties of molecular systems.  TorchANI is open-source and freely available on GitHub: https://github.com/aiqm/torchani.\n",
        "\n",
        "---\n",
        "\n",
        " **This notebook is NOT a standard protocol for parametrization!** It is just simple parametrization pipeline illustrating each step of a protocol.\n",
        "\n",
        "---\n",
        "**Bugs**\n",
        "- If you encounter any bugs, please report the issue to https://github.com/pablo-arantes/ParametrizANI/issues\n",
        "\n",
        "**Acknowledgments**\n",
        "- We would like to thank the OpenMM team for developing an excellent and open source engine.\n",
        "\n",
        "- We would like to thank the [Roitberg](https://roitberg.chem.ufl.edu/) team for developing the fantastic [TorchANI](https://github.com/aiqm/torchani).\n",
        "\n",
        "- We would like to thank the [Xavier Barril](http://www.ub.edu/bl/) team for their protocol on dihedrals parametrization and for the genetic algorithm script.\n",
        "\n",
        "- We would like to thank [iwatobipen](https://twitter.com/iwatobipen) for his fantastic [blog](https://iwatobipen.wordpress.com/) on chemoinformatics.\n",
        "\n",
        "- Also, credit to [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin.\n",
        "\n",
        "\n",
        "- Finally, we would like to thank [Making it rain](https://github.com/pablo-arantes/making-it-rain) team, **Pablo R. Arantes** ([@pablitoarantes](https://twitter.com/pablitoarantes)), **Marcelo D. Polêto** ([@mdpoleto](https://twitter.com/mdpoleto)), **Conrado Pedebos** ([@ConradoPedebos](https://twitter.com/ConradoPedebos)) and **Rodrigo Ligabue-Braun** ([@ligabue_braun](https://twitter.com/ligabue_braun)), for their amazing work.\n",
        "\n",
        "- For related notebooks see: https://github.com/pablo-arantes/ParametrizANI"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Overview:**\n",
        "\n",
        "**1.** Create the conformers for each dihedrals angles.\n",
        "\n",
        "**2.** Calculate reference 2D map for two dihedrals angles with TorchANI.\n"
      ],
      "metadata": {
        "id": "Q22JioNfAukL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Setting the environment for the calculation**\n",
        "\n",
        "Firstly, we need to install all necessary libraries and packages for our protocol. The main packages we will be installing are:\n",
        "\n",
        "1.    Anaconda (https://docs.conda.io/en/latest/miniconda.html)\n",
        "2.    OpenMM (https://openmm.org/)\n",
        "3.    RDKit (https://www.rdkit.org/)\n",
        "4.    py3Dmol (https://pypi.org/project/py3Dmol/)\n",
        "5.    Numpy (https://numpy.org/)\n",
        "6.    Matplotlib (https://matplotlib.org/)\n",
        "7.    TorchANI (https://aiqm.github.io/torchani/)"
      ],
      "metadata": {
        "id": "Ckt1G2RcpJvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Install Conda Colab**\n",
        "#@markdown It will restart the kernel (session), don't worry.\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "PHxFDwUihDg5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d52c3603-748b-4c89-cd89-6910c5c36c2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:16\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Install dependencies**\n",
        "#@markdown It will take a few minutes, please, drink a coffee and wait. ;-)\n",
        "# install dependencies\n",
        "import subprocess\n",
        "import sys\n",
        "subprocess.run(\"rm -rf /usr/local/conda-meta/pinned\", shell=True)\n",
        "subprocess.run(\"pip -q install py3Dmol\", shell=True)\n",
        "subprocess.run(\"mamba install pytorch torchvision -c pytorch\", shell=True)\n",
        "subprocess.run(\"pip install torchani\", shell=True)\n",
        "subprocess.run(\"pip install ase\", shell=True)\n",
        "subprocess.run(\"mamba install openmm=7.7.0\", shell=True)\n",
        "subprocess.run(\"mamba install openmmforcefields -c conda-forge -y\", shell=True)\n",
        "subprocess.run(\"mamba install -c conda-forge openbabel -y\", shell=True)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "TBfawTvChIKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **If you are running offline, please, run this cell to import PyTorch and TorchANI libraries**:\n",
        "\n",
        "#@markdown This step is mandatory for offline calculations!\n",
        "\n",
        "import torchani\n",
        "import torch"
      ],
      "metadata": {
        "cellView": "form",
        "id": "E2ZPve_Ea84t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Please, provide the necessary information for the molecule**:\n",
        "\n",
        "#@markdown Type the smiles or filename (PDB and MOL formats) of your molecule. **Ex: C=CC(=O)OC, molecule.pdb or molecule.mol**\n",
        "\n",
        "#@markdown Just remind you that if you want to use pdb or mol file, you should first upload the file here in Colab.\n",
        "\n",
        "#@markdown If you don't know the exactly smiles, please, check at https://pubchem.ncbi.nlm.nih.gov/\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import rdMolTransforms\n",
        "from rdkit.Chem.Draw import rdMolDraw2D\n",
        "from rdkit.Chem import rdDepictor\n",
        "from IPython.display import SVG\n",
        "import ipywidgets as widgets\n",
        "import rdkit\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "AllChem.SetPreferCoordGen(True)\n",
        "from IPython.display import Image\n",
        "from openbabel import pybel\n",
        "import os\n",
        "\n",
        "def mol_with_atom_index(mol):\n",
        "    for atom in mol.GetAtoms():\n",
        "        atom.SetAtomMapNum(atom.GetIdx())\n",
        "    return mol\n",
        "\n",
        "import py3Dmol\n",
        "\n",
        "\n",
        "Type = \"smiles\" #@param [\"smiles\", \"pdb\", \"mol\"]\n",
        "\n",
        "smiles_or_filename = 'CNC(=O)[C@H](C)NC(C)=O' #@param {type:\"string\"}\n",
        "\n",
        "workDir = '/content/' #@param {type:\"string\"}\n",
        "\n",
        "if Type == \"smiles\":\n",
        "  Smiles = smiles_or_filename\n",
        "  smiles_fig = Chem.MolFromSmiles(Smiles)\n",
        "  hmol = Chem.AddHs(smiles_fig)\n",
        "elif Type == \"pdb\":\n",
        "  mol= [m for m in pybel.readfile(filename=smiles_or_filename, format='pdb')][0]\n",
        "  out=pybel.Outputfile(filename=os.path.join(workDir,'mol.mol'),format='mol',overwrite=True)\n",
        "  out.write(mol)\n",
        "  out.close()\n",
        "  mol = Chem.MolFromMolFile(os.path.join(workDir,'mol.mol'))\n",
        "  Smiles = Chem.MolToSmiles(mol)\n",
        "  smiles_fig = Chem.MolFromSmiles(Smiles)\n",
        "  hmol = Chem.AddHs(smiles_fig)\n",
        "else:\n",
        "  mol = Chem.MolFromMolFile(smiles_or_filename)\n",
        "  Smiles = Chem.MolToSmiles(mol)\n",
        "  smiles_fig = Chem.MolFromSmiles(Smiles)\n",
        "  hmol = Chem.AddHs(smiles_fig)\n",
        "\n",
        "d2d = rdMolDraw2D.MolDraw2DSVG(600, 600)\n",
        "d2d.drawOptions().addAtomIndices=True\n",
        "d2d.DrawMolecule(hmol)\n",
        "d2d.FinishDrawing()\n",
        "svg = d2d.GetDrawingText()\n",
        "with open(os.path.join(workDir,'smiles.svg'), 'w') as f:\n",
        "    f.write(svg)\n",
        "print(\"Smiles: \" + str(Smiles))\n",
        "SVG(svg)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "cellView": "form",
        "id": "GQji-CCReEBo",
        "outputId": "22b49678-02e5-41f7-a5a8-d2ee670d4231"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Smiles: CNC(=O)[C@H](C)NC(C)=O\n"
          ]
        },
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ### **Set your dihedral angles (Geometry Optimization with MMFF94 and TorchANI)**\n",
        "#@markdown Type the number of atoms for your FIRST dihedral angle:\n",
        "from termcolor import colored\n",
        "from rdkit.Chem import rdForceFieldHelpers\n",
        "\n",
        "atom1 = '7' #@param {type:\"string\"}\n",
        "atom2 = '6' #@param {type:\"string\"}\n",
        "atom3 = '4' #@param {type:\"string\"}\n",
        "atom4 = '2' #@param {type:\"string\"}\n",
        "\n",
        "# @markdown Type the number of atoms for your SECOND dihedral angle:\n",
        "\n",
        "atom5 = '6' #@param {type:\"string\"}\n",
        "atom6 = '4' #@param {type:\"string\"}\n",
        "atom7 = '2' #@param {type:\"string\"}\n",
        "atom8 = '1' #@param {type:\"string\"}\n",
        "\n",
        "degrees_steps = \"15\" #@param [\"15\", \"20\", \"30\", \"45\", \"60\"]\n",
        "with open('degrees.dat', 'w') as f:\n",
        "  f.write(degrees_steps)\n",
        "\n",
        "# @markdown Optimization of the initial structure:\n",
        "\n",
        "optimization = \"MMFF94\" #@param [\"ANI\", \"MMFF94\"]\n",
        "\n",
        "if optimization == \"MMFF94\":\n",
        "  mol = Chem.MolFromSmiles(Smiles)\n",
        "  new_match = hmol.GetSubstructMatch(mol)\n",
        "  hmol = Chem.AddHs(mol)\n",
        "  AllChem.EmbedMolecule(hmol)\n",
        "  mp = rdForceFieldHelpers.MMFFGetMoleculeProperties(hmol)\n",
        "  ff = rdForceFieldHelpers.MMFFGetMoleculeForceField(hmol, mp)\n",
        "  for atidx in new_match:\n",
        "    ff.MMFFAddPositionConstraint(atidx,0.05,200)\n",
        "  maxIters = 10\n",
        "  while ff.Minimize(maxIts=1000) and maxIters>0:\n",
        "    maxIters -=1\n",
        "  # AllChem.OptimizeMolecule(ff, maxIters=1000)\n",
        "  # ff.Minimize()\n",
        "  charge_mol = Chem.rdPartialCharges.ComputeGasteigerCharges(hmol)\n",
        "  charge = Chem.GetFormalCharge(hmol)\n",
        "  print(\"Charge = \" + str(charge))\n",
        "  if not charge == 0:\n",
        "    print(colored(\"ANI cannot do charged system yet, stay tuned!\", 'red'))\n",
        "    exit()\n",
        "  else:\n",
        "    pass\n",
        "  mol_end = mol_with_atom_index(hmol)\n",
        "  IPythonConsole.drawMol3D(hmol)\n",
        "else:\n",
        "  from typing import List\n",
        "  from ase import Atoms\n",
        "  from ase.lattice.cubic import Diamond\n",
        "  from ase.md.langevin import Langevin\n",
        "  from ase.optimize import BFGS\n",
        "  from ase import io\n",
        "  from ase.io import read, write\n",
        "  from ase import units\n",
        "  from ase.constraints import ExternalForce, FixInternals\n",
        "  import torch\n",
        "  import torchani\n",
        "  import pandas as pd\n",
        "  import numpy as np\n",
        "  from torchani.units import HARTREE_TO_KCALMOL\n",
        "  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "  model = torchani.models.ANI2x(periodic_table_index=True).to(device)\n",
        "  calculator = torchani.models.ANI2x().ase()\n",
        "  print(\"Model = ANI2x\")\n",
        "\n",
        "\n",
        "  def mol2arr(mols, device=device):\n",
        "      coordinates = []\n",
        "      spices = []\n",
        "      for mol in mols:\n",
        "          pos = mol.GetConformer().GetPositions().tolist()\n",
        "          atomnums = [a.GetAtomicNum() for a in mol.GetAtoms()]\n",
        "          coordinates.append(pos)\n",
        "          spices.append(atomnums)\n",
        "      coordinates = torch.tensor(coordinates, requires_grad=True, device=device)\n",
        "      species = torch.tensor(spices, device=device)\n",
        "      return coordinates, species\n",
        "\n",
        "  mol = Chem.MolFromSmiles(Smiles)\n",
        "  hmol = Chem.AddHs(mol)\n",
        "  AllChem.EmbedMolecule(hmol)\n",
        "  mp = rdForceFieldHelpers.MMFFGetMoleculeProperties(hmol)\n",
        "  ff = rdForceFieldHelpers.MMFFGetMoleculeForceField(hmol, mp)\n",
        "  for atidx in mol:\n",
        "    ff.MMFFAddPositionConstraint(atidx,0.05,200)\n",
        "  maxIters = 10\n",
        "  while ff.Minimize(maxIts=1000) and maxIters>0:\n",
        "    maxIters -=1\n",
        "  # AllChem.OptimizeMolecule(ff, maxIters=1000)\n",
        "  # ff.Minimize()\n",
        "  charge_mol = Chem.rdPartialCharges.ComputeGasteigerCharges(hmol)\n",
        "  charge = Chem.GetFormalCharge(hmol)\n",
        "  print(\"Charge = \" + str(charge))\n",
        "  if not charge == 0:\n",
        "    print(colored(\"ANI cannot do charged system yet, stay tuned!\", 'red'))\n",
        "    exit()\n",
        "  else:\n",
        "    pass\n",
        "  AllChem.MolToMolFile(hmol, (os.path.join(workDir, f\"start.mol\")))\n",
        "  mol_deg = AllChem.MolFromMolFile ((os.path.join(workDir, f\"start.mol\")), removeHs=False)\n",
        "  mol = io.read(os.path.join(workDir, f\"start.mol\"))\n",
        "  coordinates, species = mol2arr([mol_deg], device)\n",
        "  tensor1 = coordinates.detach().numpy()\n",
        "  atoms = Atoms(mol, positions=tensor1[0])\n",
        "  atoms.center(vacuum=3.0)\n",
        "  atoms.set_calculator(calculator)\n",
        "  print(\"Begin minimizing \")\n",
        "  opt = BFGS(atoms)\n",
        "  opt.run(fmax=0.0001)\n",
        "  write((os.path.join(workDir, f\"start_min.xyz\")), format=\"xyz\", images=atoms)\n",
        "\n",
        "  atomic_symbols = []\n",
        "  xyz_coordinates = []\n",
        "\n",
        "  with open((os.path.join(workDir, f\"start_min.xyz\")), \"r\") as file:\n",
        "    for line_number,line in enumerate(file):\n",
        "      if line_number == 0:\n",
        "        num_atoms = int(line)\n",
        "      elif line_number == 1:\n",
        "        comment = line # might have useful information\n",
        "      else:\n",
        "        atomic_symbol, x, y, z = line.split()\n",
        "        atomic_symbols.append(atomic_symbol)\n",
        "        xyz_coordinates.append([float(x),float(y),float(z)])\n",
        "\n",
        "  from rdkit.Geometry import Point3D\n",
        "  conf = mol_deg.GetConformer()\n",
        "\n",
        "  for i in range(mol_deg.GetNumAtoms()):\n",
        "    x,y,z = xyz_coordinates[i]\n",
        "    conf.SetAtomPosition(i,Point3D(x,y,z))\n",
        "  AllChem.MolToMolFile(mol_deg, (os.path.join(workDir, f\"start_min.mol\")))\n",
        "\n",
        "\n",
        "  hmol = AllChem.MolFromMolFile ((os.path.join(workDir, f\"start_min.mol\")), removeHs=False)\n",
        "  mol_end = mol_with_atom_index(hmol)\n",
        "  IPythonConsole.drawMol3D(hmol)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "73ur9enaevk7",
        "outputId": "5413995e-76cf-4de1-b35f-5c379b18a7fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Charge = 0\n"
          ]
        },
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "#@title ### **Create the conformers based on your dihedrals angles**\n",
        "#@markdown Run this cell to create the conformers based on your selections from previous cell.\n",
        "\n",
        "#@markdown **Important**: If you check the **mol_files** folder, all of your conformers will be there.\n",
        "\n",
        "\n",
        "#Create the conformers and save in the folder\n",
        "import copy\n",
        "import numpy as np\n",
        "from IPython.display import display\n",
        "import glob\n",
        "\n",
        "if os.path.exists(os.path.join(workDir, \"mol_files\")):\n",
        "  for f in glob.glob(os.path.join(workDir, \"mol_files/*.mol\")):\n",
        "    os.remove(f)\n",
        "else:\n",
        "  os.mkdir(os.path.join(workDir, \"mol_files\"))\n",
        "\n",
        "confs = []\n",
        "filenames = []\n",
        "deg_points = range(-180, (180+int(degrees_steps)), int(degrees_steps))\n",
        "\n",
        "if optimization == \"ANI\":\n",
        "  hmol = AllChem.MolFromMolFile ((os.path.join(workDir, f\"start_min.mol\")), removeHs=False)\n",
        "else:\n",
        "  pass\n",
        "\n",
        "for i in range(-180, (180+int(degrees_steps)), int(degrees_steps)):\n",
        "  for deg1, deg2 in zip(np.repeat(i, len(deg_points)), range(-180, (180+int(degrees_steps)), int(degrees_steps))):\n",
        "      rdMolTransforms.SetDihedralDeg(hmol.GetConformer(0), int(atom1), int(atom2), int(atom3), int(atom4), i)\n",
        "      rdMolTransforms.SetDihedralDeg(hmol.GetConformer(0), int(atom5), int(atom6), int(atom7), int(atom8), deg2)\n",
        "      mp = rdForceFieldHelpers.MMFFGetMoleculeProperties(hmol)\n",
        "      ff = rdForceFieldHelpers.MMFFGetMoleculeForceField(hmol, mp)\n",
        "      ff.MMFFAddTorsionConstraint(int(atom1), int(atom2), int(atom3), int(atom4), False, i, i, 1000.0)\n",
        "      ff.MMFFAddTorsionConstraint(int(atom5), int(atom6), int(atom7), int(atom8), False, deg2, deg2, 1000.0)\n",
        "      for atidx in new_match:\n",
        "        ff.MMFFAddPositionConstraint(atidx,0.05,200)\n",
        "      maxIters = 10\n",
        "      while ff.Minimize(maxIts=1000) and maxIters>0:\n",
        "        maxIters -=1\n",
        "      AllChem.MolToMolFile(hmol, (os.path.join(workDir, f\"mol_files/{deg1}_{deg2}.mol\")))\n",
        "      AllChem.MolToPDBFile(hmol, (os.path.join(workDir, f\"mol_files/{deg1}_{deg2}.pdb\")))\n",
        "      # if deg1 % 60 == 0 and deg2 % 60 == 0:\n",
        "      #     print(deg1,deg2)\n",
        "      #     display(IPythonConsole.drawMol3D(hmol))\n"
      ],
      "metadata": {
        "id": "LeWultOSfcXF",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Calculate reference energy profile using TorchANI**:\n",
        "\n",
        "#@markdown Structural Optimization and energy calculation of each conformer, using TorchANI as the optimizing engine.\n",
        "#@markdown TorchANI will be used to obtain the energy profile of the dihedrals angles .\n",
        "\n",
        "#@markdown ANI is a general-purpose neural network-based atomistic potential for organic molecules. Four ANI models have been published, the ANI-1, ANI-1x, ANI-1ccx, and ANI-2x potentials. The ANI-1 model was developed by random sampling conformational space of 57k organic molecules with up to eight heavy atoms, C, N, and O, plus H atoms to have proper chemistry, then running DFT calculations to obtain potential energies for training. ANI-1x was trained to a data set of molecular conformations sampled through an active learning scheme. Active learning is where the model itself is iteratively used to decide what new data should be included in the next iteration. ANI-1ccx was trained to the ANI-1x data set, then retrained to a 10% smaller data set of accurate coupled cluster calculations, resulting in a potential that outperformed DFT in test cases. ANI-2x was trained by adding many millions of data points to the ANI-1x data set, at the same level of theory, but including elements S, F, and Cl. We included the **ANI-1x**, **ANI-1ccx**, and **ANI-2x** models in our notebook.\n",
        "\n",
        "#@markdown If you want to know more about **TorchANI**, please, check at https://aiqm.github.io/torchani/\n",
        "\n",
        "#@markdown Please, select your ANI model:\n",
        "model_name = \"ANI-2x\" #@param [\"ANI-1x\", \"ANI-1ccx\", \"ANI-2x\"]\n",
        "optimization = \"No\" #@param [\"Yes\", \"No\"]\n",
        "#@markdown **Important:**The optimization will take 1 hour or more, depends on the size of your molecule.\n",
        "\n",
        "\n",
        "#@markdown Convergence threshold for geometry optimization:\n",
        "\n",
        "opt_tol = 0.005 #@param {type:\"slider\", min:0.0001, max:0.01, step:0.0001}\n",
        "\n",
        "from typing import List\n",
        "from ase import Atoms\n",
        "from ase.lattice.cubic import Diamond\n",
        "from ase.md.langevin import Langevin\n",
        "from ase.optimize import BFGS\n",
        "from ase import io\n",
        "from ase.io import read, write\n",
        "from ase import units\n",
        "from ase.constraints import ExternalForce, FixInternals\n",
        "import torch\n",
        "import torchani\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torchani.units import HARTREE_TO_KCALMOL\n",
        "# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "# device = torch.device('cpu')\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "if model_name == \"ANI-2x\":\n",
        "  model = torchani.models.ANI2x(periodic_table_index=True).to(device)\n",
        "  calculator = torchani.models.ANI2x().ase()\n",
        "  print(\"Model = ANI2x\")\n",
        "elif model_name == \"ANI-1ccx\":\n",
        "  model = torchani.models.ANI1ccx(periodic_table_index=True).to(device)\n",
        "  calculator = torchani.models.ANI1ccx().ase()\n",
        "  print(\"Model = ANI1ccx\")\n",
        "elif model_name == \"ANI-1x\":\n",
        "  model = torchani.models.ANI1x(periodic_table_index=True).to(device)\n",
        "  calculator = torchani.models.ANI1x().ase()\n",
        "  print(\"Model = ANI1x\")\n",
        "else:\n",
        "  pass\n",
        "\n",
        "def mol2arr(mols, device=device):\n",
        "    coordinates = []\n",
        "    spices = []\n",
        "    for mol in mols:\n",
        "        pos = mol.GetConformer().GetPositions().tolist()\n",
        "        atomnums = [a.GetAtomicNum() for a in mol.GetAtoms()]\n",
        "        coordinates.append(pos)\n",
        "        spices.append(atomnums)\n",
        "    coordinates = torch.tensor(coordinates,\n",
        "                               requires_grad=True,\n",
        "                               device=device)\n",
        "    species = torch.tensor(spices, device=device)\n",
        "    return coordinates, species\n",
        "\n",
        "deg_points = range(-180, (180+int(degrees_steps)), int(degrees_steps))\n",
        "\n",
        "if optimization == \"Yes\":\n",
        "  for i in range(-180, (180+int(degrees_steps)), int(degrees_steps)):\n",
        "    for deg1, deg2 in zip(np.repeat(i, len(deg_points)), range(-180, (180+int(degrees_steps)), int(degrees_steps))):\n",
        "      mol_deg = AllChem.MolFromMolFile ((os.path.join(workDir, f\"mol_files/{deg1}_{deg2}.mol\")), removeHs=False)\n",
        "      mol = io.read(os.path.join(workDir, f\"mol_files/{deg1}_{deg2}.mol\"))\n",
        "      coordinates, species = mol2arr([mol_deg], device)\n",
        "      tensor1 = coordinates.detach().numpy()\n",
        "      atoms = Atoms(mol, positions=tensor1[0])\n",
        "      atoms.center(vacuum=3.0)\n",
        "      dihedral_indices1 = [int(atom1), int(atom2), int(atom3), int(atom4)]\n",
        "      dihedral_indices2 = [int(atom5), int(atom6), int(atom7), int(atom8)]\n",
        "      dihedral1 = [atoms.get_dihedral(*dihedral_indices1), dihedral_indices1]\n",
        "      dihedral2 = [atoms.get_dihedral(*dihedral_indices2), dihedral_indices2]\n",
        "      c = FixInternals(dihedrals_deg=[dihedral1,dihedral2])\n",
        "      atoms.set_constraint(c)\n",
        "      atoms.set_calculator(calculator)\n",
        "      print(\"Begin minimizing \" + f\"mol_files/{deg1}_{deg2}.mol\" )\n",
        "      opt = BFGS(atoms)\n",
        "      opt.run(fmax=opt_tol)\n",
        "      # print()\n",
        "      write((os.path.join(workDir, f\"mol_files/{deg1}_{deg2}_min.xyz\")), format=\"xyz\", images=atoms)\n",
        "\n",
        "      atomic_symbols = []\n",
        "      xyz_coordinates = []\n",
        "\n",
        "      with open((os.path.join(workDir, f\"mol_files/{deg1}_{deg2}_min.xyz\")), \"r\") as file:\n",
        "        for line_number,line in enumerate(file):\n",
        "            if line_number == 0:\n",
        "                num_atoms = int(line)\n",
        "            elif line_number == 1:\n",
        "                comment = line # might have useful information\n",
        "            else:\n",
        "                atomic_symbol, x, y, z = line.split()\n",
        "                atomic_symbols.append(atomic_symbol)\n",
        "                xyz_coordinates.append([float(x),float(y),float(z)])\n",
        "\n",
        "      from rdkit.Geometry import Point3D\n",
        "      conf = mol_deg.GetConformer()\n",
        "\n",
        "      for i in range(mol_deg.GetNumAtoms()):\n",
        "        x,y,z = xyz_coordinates[i]\n",
        "        conf.SetAtomPosition(i,Point3D(x,y,z))\n",
        "      AllChem.MolToMolFile(mol_deg, (os.path.join(workDir, f\"mol_files/{deg1}_{deg2}_min.mol\")))\n",
        "      AllChem.MolToPDBFile(mol_deg, (os.path.join(workDir, f\"mol_files/{deg1}_{deg2}_min.pdb\")))\n",
        "  mol_energy = \"_min.mol\"\n",
        "else:\n",
        "  mol_energy = \".mol\"\n",
        "\n",
        "#TorchANI Energies\n",
        "energy_list = []\n",
        "dihedral_degrees1 = []\n",
        "dihedral_degrees2 = []\n",
        "for i in range(-180, (180+int(degrees_steps)), int(degrees_steps)):\n",
        "  for deg1, deg2 in zip(np.repeat(i, len(deg_points)), range(-180, (180+int(degrees_steps)), int(degrees_steps))):\n",
        "    mol_deg = AllChem.MolFromMolFile ((os.path.join(workDir, f\"mol_files/{deg1}_{deg2}\" + str(mol_energy))), removeHs=False)\n",
        "    coordinates, species = mol2arr([mol_deg], device)\n",
        "    energy = model((species, coordinates)).energies\n",
        "    derivative = torch.autograd.grad(energy.sum(), coordinates)[0]\n",
        "    force = -derivative\n",
        "    energy_list.append(energy.item())\n",
        "    dihedral_degrees1.append(deg1)\n",
        "    dihedral_degrees2.append(deg2)\n",
        "    # print(deg1, deg2, energy.item())\n",
        "\n",
        "#Calculates the relative energy\n",
        "ani_energies = (np.array(energy_list) - min(energy_list)) * HARTREE_TO_KCALMOL\n",
        "\n",
        "# with open(os.path.join(workDir,\"ani.dat\"), 'w') as f:\n",
        "#     for item in ani_energies:\n",
        "#         f.write(\"%s\\n\" % item)\n",
        "\n",
        "print(\"Dihedral1 Dihedral2   Energy\")\n",
        "with open('ani.dat', 'w') as f:\n",
        "    for item1, item2, item3 in zip(dihedral_degrees1, dihedral_degrees2, ani_energies):\n",
        "      degree_str1 = str(\"{:.0f}\".format(item1))\n",
        "      degree_str2 = str(\"{:.0f}\".format(item2))\n",
        "      energy_str = str(\"{:.3f}\".format(item3))\n",
        "      string = degree_str1.rjust(8, \" \") + degree_str2.rjust(8, \" \") + \"      \" + energy_str.ljust(8, \" \") + \" kcal/mol\"\n",
        "      print(string)\n",
        "      f.write(string + \"\\n\")\n",
        "      # f.write(\"%s\\n\" % item3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "EAS-PIIKkvcN",
        "outputId": "1f9b698c-5ef3-499e-8f95-2819a4eecb30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model = ANI2x\n",
            "Dihedral1 Dihedral2   Energy\n",
            "    -180    -180      2.661    kcal/mol\n",
            "    -180    -165      3.527    kcal/mol\n",
            "    -180    -150      4.384    kcal/mol\n",
            "    -180    -135      5.123    kcal/mol\n",
            "    -180    -120      5.969    kcal/mol\n",
            "    -180    -105      6.814    kcal/mol\n",
            "    -180     -90      7.312    kcal/mol\n",
            "    -180     -75      7.246    kcal/mol\n",
            "    -180     -60      6.952    kcal/mol\n",
            "    -180     -45      6.358    kcal/mol\n",
            "    -180     -30      5.783    kcal/mol\n",
            "    -180     -15      6.259    kcal/mol\n",
            "    -180       0      7.813    kcal/mol\n",
            "    -180      15      9.954    kcal/mol\n",
            "    -180      30      5.673    kcal/mol\n",
            "    -180      45      5.120    kcal/mol\n",
            "    -180      60      5.137    kcal/mol\n",
            "    -180      75      5.410    kcal/mol\n",
            "    -180      90      5.690    kcal/mol\n",
            "    -180     105      5.254    kcal/mol\n",
            "    -180     120      4.222    kcal/mol\n",
            "    -180     135      3.164    kcal/mol\n",
            "    -180     150      2.531    kcal/mol\n",
            "    -180     165      2.293    kcal/mol\n",
            "    -180     180      2.650    kcal/mol\n",
            "    -165    -180      1.922    kcal/mol\n",
            "    -165    -165      2.690    kcal/mol\n",
            "    -165    -150      3.691    kcal/mol\n",
            "    -165    -135      4.623    kcal/mol\n",
            "    -165    -120      5.576    kcal/mol\n",
            "    -165    -105      6.494    kcal/mol\n",
            "    -165     -90      6.824    kcal/mol\n",
            "    -165     -75      6.671    kcal/mol\n",
            "    -165     -60      5.975    kcal/mol\n",
            "    -165     -45      5.342    kcal/mol\n",
            "    -165     -30      5.134    kcal/mol\n",
            "    -165     -15      5.939    kcal/mol\n",
            "    -165       0      7.877    kcal/mol\n",
            "    -165      15      10.254   kcal/mol\n",
            "    -165      30      4.405    kcal/mol\n",
            "    -165      45      3.977    kcal/mol\n",
            "    -165      60      4.065    kcal/mol\n",
            "    -165      75      4.464    kcal/mol\n",
            "    -165      90      4.795    kcal/mol\n",
            "    -165     105      4.327    kcal/mol\n",
            "    -165     120      3.348    kcal/mol\n",
            "    -165     135      2.318    kcal/mol\n",
            "    -165     150      1.727    kcal/mol\n",
            "    -165     165      1.512    kcal/mol\n",
            "    -165     180      1.842    kcal/mol\n",
            "    -150    -180      1.847    kcal/mol\n",
            "    -150    -165      2.829    kcal/mol\n",
            "    -150    -150      3.939    kcal/mol\n",
            "    -150    -135      4.835    kcal/mol\n",
            "    -150    -120      5.639    kcal/mol\n",
            "    -150    -105      6.240    kcal/mol\n",
            "    -150     -90      6.113    kcal/mol\n",
            "    -150     -75      5.522    kcal/mol\n",
            "    -150     -60      5.421    kcal/mol\n",
            "    -150     -45      5.262    kcal/mol\n",
            "    -150     -30      5.517    kcal/mol\n",
            "    -150     -15      6.753    kcal/mol\n",
            "    -150       0      8.886    kcal/mol\n",
            "    -150      15      4.174    kcal/mol\n",
            "    -150      30      3.504    kcal/mol\n",
            "    -150      45      3.672    kcal/mol\n",
            "    -150      60      4.070    kcal/mol\n",
            "    -150      75      4.668    kcal/mol\n",
            "    -150      90      4.936    kcal/mol\n",
            "    -150     105      4.365    kcal/mol\n",
            "    -150     120      3.333    kcal/mol\n",
            "    -150     135      2.271    kcal/mol\n",
            "    -150     150      1.675    kcal/mol\n",
            "    -150     165      1.472    kcal/mol\n",
            "    -150     180      1.813    kcal/mol\n",
            "    -135    -180      2.780    kcal/mol\n",
            "    -135    -165      3.963    kcal/mol\n",
            "    -135    -150      5.363    kcal/mol\n",
            "    -135    -135      6.223    kcal/mol\n",
            "    -135    -120      6.686    kcal/mol\n",
            "    -135    -105      6.745    kcal/mol\n",
            "    -135     -90      6.403    kcal/mol\n",
            "    -135     -75      5.940    kcal/mol\n",
            "    -135     -60      6.086    kcal/mol\n",
            "    -135     -45      6.293    kcal/mol\n",
            "    -135     -30      6.981    kcal/mol\n",
            "    -135     -15      8.467    kcal/mol\n",
            "    -135       0      5.100    kcal/mol\n",
            "    -135      15      4.139    kcal/mol\n",
            "    -135      30      3.894    kcal/mol\n",
            "    -135      45      4.009    kcal/mol\n",
            "    -135      60      4.217    kcal/mol\n",
            "    -135      75      4.702    kcal/mol\n",
            "    -135      90      5.088    kcal/mol\n",
            "    -135     105      4.712    kcal/mol\n",
            "    -135     120      3.587    kcal/mol\n",
            "    -135     135      2.474    kcal/mol\n",
            "    -135     150      1.976    kcal/mol\n",
            "    -135     165      2.039    kcal/mol\n",
            "    -135     180      2.791    kcal/mol\n",
            "    -120    -180      3.715    kcal/mol\n",
            "    -120    -165      5.164    kcal/mol\n",
            "    -120    -150      6.636    kcal/mol\n",
            "    -120    -135      7.366    kcal/mol\n",
            "    -120    -120      7.453    kcal/mol\n",
            "    -120    -105      7.235    kcal/mol\n",
            "    -120     -90      6.805    kcal/mol\n",
            "    -120     -75      6.469    kcal/mol\n",
            "    -120     -60      6.884    kcal/mol\n",
            "    -120     -45      7.413    kcal/mol\n",
            "    -120     -30      8.151    kcal/mol\n",
            "    -120     -15      5.632    kcal/mol\n",
            "    -120       0      4.827    kcal/mol\n",
            "    -120      15      4.301    kcal/mol\n",
            "    -120      30      4.293    kcal/mol\n",
            "    -120      45      4.140    kcal/mol\n",
            "    -120      60      3.899    kcal/mol\n",
            "    -120      75      4.179    kcal/mol\n",
            "    -120      90      4.540    kcal/mol\n",
            "    -120     105      4.931    kcal/mol\n",
            "    -120     120      4.317    kcal/mol\n",
            "    -120     135      3.198    kcal/mol\n",
            "    -120     150      2.488    kcal/mol\n",
            "    -120     165      2.773    kcal/mol\n",
            "    -120     180      3.717    kcal/mol\n",
            "    -105    -180      4.596    kcal/mol\n",
            "    -105    -165      5.933    kcal/mol\n",
            "    -105    -150      7.128    kcal/mol\n",
            "    -105    -135      7.569    kcal/mol\n",
            "    -105    -120      7.542    kcal/mol\n",
            "    -105    -105      7.312    kcal/mol\n",
            "    -105     -90      6.942    kcal/mol\n",
            "    -105     -75      6.995    kcal/mol\n",
            "    -105     -60      7.776    kcal/mol\n",
            "    -105     -45      8.407    kcal/mol\n",
            "    -105     -30      6.279    kcal/mol\n",
            "    -105     -15      5.475    kcal/mol\n",
            "    -105       0      5.092    kcal/mol\n",
            "    -105      15      5.147    kcal/mol\n",
            "    -105      30      4.721    kcal/mol\n",
            "    -105      45      3.672    kcal/mol\n",
            "    -105      60      2.543    kcal/mol\n",
            "    -105      75      2.286    kcal/mol\n",
            "    -105      90      2.979    kcal/mol\n",
            "    -105     105      4.214    kcal/mol\n",
            "    -105     120      4.654    kcal/mol\n",
            "    -105     135      3.863    kcal/mol\n",
            "    -105     150      3.326    kcal/mol\n",
            "    -105     165      3.623    kcal/mol\n",
            "    -105     180      4.596    kcal/mol\n",
            "     -90    -180      4.908    kcal/mol\n",
            "     -90    -165      5.998    kcal/mol\n",
            "     -90    -150      7.099    kcal/mol\n",
            "     -90    -135      7.632    kcal/mol\n",
            "     -90    -120      7.799    kcal/mol\n",
            "     -90    -105      7.574    kcal/mol\n",
            "     -90     -90      7.128    kcal/mol\n",
            "     -90     -75      7.322    kcal/mol\n",
            "     -90     -60      8.230    kcal/mol\n",
            "     -90     -45      8.243    kcal/mol\n",
            "     -90     -30      5.707    kcal/mol\n",
            "     -90     -15      5.500    kcal/mol\n",
            "     -90       0      5.923    kcal/mol\n",
            "     -90      15      5.500    kcal/mol\n",
            "     -90      30      3.999    kcal/mol\n",
            "     -90      45      2.300    kcal/mol\n",
            "     -90      60      0.868    kcal/mol\n",
            "     -90      75      0.342    kcal/mol\n",
            "     -90      90      1.184    kcal/mol\n",
            "     -90     105      2.649    kcal/mol\n",
            "     -90     120      4.277    kcal/mol\n",
            "     -90     135      4.448    kcal/mol\n",
            "     -90     150      3.984    kcal/mol\n",
            "     -90     165      4.130    kcal/mol\n",
            "     -90     180      4.907    kcal/mol\n",
            "     -75    -180      5.325    kcal/mol\n",
            "     -75    -165      6.587    kcal/mol\n",
            "     -75    -150      8.039    kcal/mol\n",
            "     -75    -135      8.985    kcal/mol\n",
            "     -75    -120      9.156    kcal/mol\n",
            "     -75    -105      8.856    kcal/mol\n",
            "     -75     -90      8.182    kcal/mol\n",
            "     -75     -75      7.839    kcal/mol\n",
            "     -75     -60      8.236    kcal/mol\n",
            "     -75     -45      5.563    kcal/mol\n",
            "     -75     -30      5.701    kcal/mol\n",
            "     -75     -15      6.432    kcal/mol\n",
            "     -75       0      6.935    kcal/mol\n",
            "     -75      15      5.645    kcal/mol\n",
            "     -75      30      3.457    kcal/mol\n",
            "     -75      45      2.037    kcal/mol\n",
            "     -75      60      0.521    kcal/mol\n",
            "     -75      75      0.000    kcal/mol\n",
            "     -75      90      0.499    kcal/mol\n",
            "     -75     105      1.953    kcal/mol\n",
            "     -75     120      3.655    kcal/mol\n",
            "     -75     135      4.510    kcal/mol\n",
            "     -75     150      4.225    kcal/mol\n",
            "     -75     165      4.515    kcal/mol\n",
            "     -75     180      5.322    kcal/mol\n",
            "     -60    -180      7.184    kcal/mol\n",
            "     -60    -165      9.020    kcal/mol\n",
            "     -60    -150      10.747   kcal/mol\n",
            "     -60    -135      11.739   kcal/mol\n",
            "     -60    -120      11.903   kcal/mol\n",
            "     -60    -105      11.259   kcal/mol\n",
            "     -60     -90      9.840    kcal/mol\n",
            "     -60     -75      8.703    kcal/mol\n",
            "     -60     -60      6.340    kcal/mol\n",
            "     -60     -45      5.326    kcal/mol\n",
            "     -60     -30      5.797    kcal/mol\n",
            "     -60     -15      7.412    kcal/mol\n",
            "     -60       0      7.965    kcal/mol\n",
            "     -60      15      6.111    kcal/mol\n",
            "     -60      30      4.674    kcal/mol\n",
            "     -60      45      3.656    kcal/mol\n",
            "     -60      60      2.455    kcal/mol\n",
            "     -60      75      1.567    kcal/mol\n",
            "     -60      90      1.588    kcal/mol\n",
            "     -60     105      2.561    kcal/mol\n",
            "     -60     120      3.888    kcal/mol\n",
            "     -60     135      4.808    kcal/mol\n",
            "     -60     150      5.107    kcal/mol\n",
            "     -60     165      5.804    kcal/mol\n",
            "     -60     180      7.159    kcal/mol\n",
            "     -45    -180      10.961   kcal/mol\n",
            "     -45    -165      13.392   kcal/mol\n",
            "     -45    -150      15.346   kcal/mol\n",
            "     -45    -135      16.053   kcal/mol\n",
            "     -45    -120      15.720   kcal/mol\n",
            "     -45    -105      14.312   kcal/mol\n",
            "     -45     -90      12.021   kcal/mol\n",
            "     -45     -75      9.587    kcal/mol\n",
            "     -45     -60      7.623    kcal/mol\n",
            "     -45     -45      6.891    kcal/mol\n",
            "     -45     -30      8.193    kcal/mol\n",
            "     -45     -15      10.858   kcal/mol\n",
            "     -45       0      10.975   kcal/mol\n",
            "     -45      15      10.101   kcal/mol\n",
            "     -45      30      8.601    kcal/mol\n",
            "     -45      45      7.397    kcal/mol\n",
            "     -45      60      6.052    kcal/mol\n",
            "     -45      75      4.785    kcal/mol\n",
            "     -45      90      4.366    kcal/mol\n",
            "     -45     105      5.022    kcal/mol\n",
            "     -45     120      6.275    kcal/mol\n",
            "     -45     135      6.992    kcal/mol\n",
            "     -45     150      7.473    kcal/mol\n",
            "     -45     165      8.813    kcal/mol\n",
            "     -45     180      10.960   kcal/mol\n",
            "     -30    -180      16.003   kcal/mol\n",
            "     -30    -165      19.040   kcal/mol\n",
            "     -30    -150      20.967   kcal/mol\n",
            "     -30    -135      20.906   kcal/mol\n",
            "     -30    -120      19.430   kcal/mol\n",
            "     -30    -105      17.710   kcal/mol\n",
            "     -30     -90      14.814   kcal/mol\n",
            "     -30     -75      11.262   kcal/mol\n",
            "     -30     -60      8.797    kcal/mol\n",
            "     -30     -45      9.205    kcal/mol\n",
            "     -30     -30      11.856   kcal/mol\n",
            "     -30     -15      14.110   kcal/mol\n",
            "     -30       0      14.883   kcal/mol\n",
            "     -30      15      13.997   kcal/mol\n",
            "     -30      30      12.708   kcal/mol\n",
            "     -30      45      11.421   kcal/mol\n",
            "     -30      60      9.477    kcal/mol\n",
            "     -30      75      8.150    kcal/mol\n",
            "     -30      90      8.123    kcal/mol\n",
            "     -30     105      8.661    kcal/mol\n",
            "     -30     120      9.420    kcal/mol\n",
            "     -30     135      10.004   kcal/mol\n",
            "     -30     150      10.989   kcal/mol\n",
            "     -30     165      13.325   kcal/mol\n",
            "     -30     180      16.328   kcal/mol\n",
            "     -15    -180      22.289   kcal/mol\n",
            "     -15    -165      25.478   kcal/mol\n",
            "     -15    -150      26.643   kcal/mol\n",
            "     -15    -135      24.674   kcal/mol\n",
            "     -15    -120      21.911   kcal/mol\n",
            "     -15    -105      18.365   kcal/mol\n",
            "     -15     -90      14.637   kcal/mol\n",
            "     -15     -75      11.013   kcal/mol\n",
            "     -15     -60      9.995    kcal/mol\n",
            "     -15     -45      11.524   kcal/mol\n",
            "     -15     -30      14.209   kcal/mol\n",
            "     -15     -15      16.517   kcal/mol\n",
            "     -15       0      16.778   kcal/mol\n",
            "     -15      15      15.194   kcal/mol\n",
            "     -15      30      14.279   kcal/mol\n",
            "     -15      45      12.441   kcal/mol\n",
            "     -15      60      11.747   kcal/mol\n",
            "     -15      75      10.940   kcal/mol\n",
            "     -15      90      10.883   kcal/mol\n",
            "     -15     105      12.845   kcal/mol\n",
            "     -15     120      13.363   kcal/mol\n",
            "     -15     135      14.060   kcal/mol\n",
            "     -15     150      15.790   kcal/mol\n",
            "     -15     165      19.049   kcal/mol\n",
            "     -15     180      22.794   kcal/mol\n",
            "       0    -180      26.780   kcal/mol\n",
            "       0    -165      28.314   kcal/mol\n",
            "       0    -150      26.688   kcal/mol\n",
            "       0    -135      22.841   kcal/mol\n",
            "       0    -120      19.457   kcal/mol\n",
            "       0    -105      17.273   kcal/mol\n",
            "       0     -90      13.506   kcal/mol\n",
            "       0     -75      11.721   kcal/mol\n",
            "       0     -60      11.022   kcal/mol\n",
            "       0     -45      13.011   kcal/mol\n",
            "       0     -30      15.702   kcal/mol\n",
            "       0     -15      16.887   kcal/mol\n",
            "       0       0      15.916   kcal/mol\n",
            "       0      15      15.023   kcal/mol\n",
            "       0      30      13.690   kcal/mol\n",
            "       0      45      12.043   kcal/mol\n",
            "       0      60      10.941   kcal/mol\n",
            "       0      75      10.552   kcal/mol\n",
            "       0      90      10.930   kcal/mol\n",
            "       0     105      13.355   kcal/mol\n",
            "       0     120      14.858   kcal/mol\n",
            "       0     135      17.402   kcal/mol\n",
            "       0     150      19.892   kcal/mol\n",
            "       0     165      23.559   kcal/mol\n",
            "       0     180      26.936   kcal/mol\n",
            "      15    -180      26.813   kcal/mol\n",
            "      15    -165      25.636   kcal/mol\n",
            "      15    -150      24.478   kcal/mol\n",
            "      15    -135      20.063   kcal/mol\n",
            "      15    -120      16.977   kcal/mol\n",
            "      15    -105      13.860   kcal/mol\n",
            "      15     -90      10.917   kcal/mol\n",
            "      15     -75      10.246   kcal/mol\n",
            "      15     -60      11.586   kcal/mol\n",
            "      15     -45      14.075   kcal/mol\n",
            "      15     -30      15.492   kcal/mol\n",
            "      15     -15      14.860   kcal/mol\n",
            "      15       0      13.878   kcal/mol\n",
            "      15      15      13.349   kcal/mol\n",
            "      15      30      12.248   kcal/mol\n",
            "      15      45      11.349   kcal/mol\n",
            "      15      60      10.425   kcal/mol\n",
            "      15      75      10.019   kcal/mol\n",
            "      15      90      11.714   kcal/mol\n",
            "      15     105      14.487   kcal/mol\n",
            "      15     120      16.952   kcal/mol\n",
            "      15     135      19.478   kcal/mol\n",
            "      15     150      22.335   kcal/mol\n",
            "      15     165      25.384   kcal/mol\n",
            "      15     180      26.470   kcal/mol\n",
            "      30    -180      22.283   kcal/mol\n",
            "      30    -165      20.345   kcal/mol\n",
            "      30    -150      17.448   kcal/mol\n",
            "      30    -135      14.981   kcal/mol\n",
            "      30    -120      12.642   kcal/mol\n",
            "      30    -105      10.004   kcal/mol\n",
            "      30     -90      8.167    kcal/mol\n",
            "      30     -75      8.545    kcal/mol\n",
            "      30     -60      10.478   kcal/mol\n",
            "      30     -45      11.927   kcal/mol\n",
            "      30     -30      11.923   kcal/mol\n",
            "      30     -15      11.600   kcal/mol\n",
            "      30       0      11.445   kcal/mol\n",
            "      30      15      11.189   kcal/mol\n",
            "      30      30      10.846   kcal/mol\n",
            "      30      45      9.832    kcal/mol\n",
            "      30      60      8.732    kcal/mol\n",
            "      30      75      9.215    kcal/mol\n",
            "      30      90      12.023   kcal/mol\n",
            "      30     105      15.162   kcal/mol\n",
            "      30     120      17.987   kcal/mol\n",
            "      30     135      20.108   kcal/mol\n",
            "      30     150      21.571   kcal/mol\n",
            "      30     165      21.709   kcal/mol\n",
            "      30     180      20.280   kcal/mol\n",
            "      45    -180      14.692   kcal/mol\n",
            "      45    -165      12.931   kcal/mol\n",
            "      45    -150      11.327   kcal/mol\n",
            "      45    -135      10.041   kcal/mol\n",
            "      45    -120      8.350    kcal/mol\n",
            "      45    -105      6.142    kcal/mol\n",
            "      45     -90      5.282    kcal/mol\n",
            "      45     -75      6.032    kcal/mol\n",
            "      45     -60      7.194    kcal/mol\n",
            "      45     -45      7.606    kcal/mol\n",
            "      45     -30      7.765    kcal/mol\n",
            "      45     -15      7.990    kcal/mol\n",
            "      45       0      8.431    kcal/mol\n",
            "      45      15      9.065    kcal/mol\n",
            "      45      30      8.458    kcal/mol\n",
            "      45      45      6.884    kcal/mol\n",
            "      45      60      6.473    kcal/mol\n",
            "      45      75      8.186    kcal/mol\n",
            "      45      90      11.327   kcal/mol\n",
            "      45     105      14.164   kcal/mol\n",
            "      45     120      15.998   kcal/mol\n",
            "      45     135      16.301   kcal/mol\n",
            "      45     150      15.512   kcal/mol\n",
            "      45     165      14.006   kcal/mol\n",
            "      45     180      12.566   kcal/mol\n",
            "      60    -180      9.128    kcal/mol\n",
            "      60    -165      8.158    kcal/mol\n",
            "      60    -150      7.771    kcal/mol\n",
            "      60    -135      7.453    kcal/mol\n",
            "      60    -120      6.292    kcal/mol\n",
            "      60    -105      4.606    kcal/mol\n",
            "      60     -90      3.586    kcal/mol\n",
            "      60     -75      3.442    kcal/mol\n",
            "      60     -60      3.924    kcal/mol\n",
            "      60     -45      4.487    kcal/mol\n",
            "      60     -30      5.026    kcal/mol\n",
            "      60     -15      5.696    kcal/mol\n",
            "      60       0      6.702    kcal/mol\n",
            "      60      15      6.996    kcal/mol\n",
            "      60      30      5.827    kcal/mol\n",
            "      60      45      4.857    kcal/mol\n",
            "      60      60      6.023    kcal/mol\n",
            "      60      75      7.626    kcal/mol\n",
            "      60      90      9.866    kcal/mol\n",
            "      60     105      12.338   kcal/mol\n",
            "      60     120      12.926   kcal/mol\n",
            "      60     135      12.974   kcal/mol\n",
            "      60     150      11.480   kcal/mol\n",
            "      60     165      10.238   kcal/mol\n",
            "      60     180      9.110    kcal/mol\n",
            "      75    -180      7.792    kcal/mol\n",
            "      75    -165      7.292    kcal/mol\n",
            "      75    -150      7.456    kcal/mol\n",
            "      75    -135      7.898    kcal/mol\n",
            "      75    -120      7.340    kcal/mol\n",
            "      75    -105      5.319    kcal/mol\n",
            "      75     -90      3.473    kcal/mol\n",
            "      75     -75      2.427    kcal/mol\n",
            "      75     -60      2.608    kcal/mol\n",
            "      75     -45      3.479    kcal/mol\n",
            "      75     -30      4.632    kcal/mol\n",
            "      75     -15      5.916    kcal/mol\n",
            "      75       0      6.650    kcal/mol\n",
            "      75      15      6.186    kcal/mol\n",
            "      75      30      5.401    kcal/mol\n",
            "      75      45      5.442    kcal/mol\n",
            "      75      60      6.612    kcal/mol\n",
            "      75      75      7.662    kcal/mol\n",
            "      75      90      8.861    kcal/mol\n",
            "      75     105      9.841    kcal/mol\n",
            "      75     120      10.151   kcal/mol\n",
            "      75     135      9.653    kcal/mol\n",
            "      75     150      8.845    kcal/mol\n",
            "      75     165      8.026    kcal/mol\n",
            "      75     180      7.563    kcal/mol\n",
            "      90    -180      8.264    kcal/mol\n",
            "      90    -165      7.767    kcal/mol\n",
            "      90    -150      7.877    kcal/mol\n",
            "      90    -135      8.510    kcal/mol\n",
            "      90    -120      8.910    kcal/mol\n",
            "      90    -105      7.690    kcal/mol\n",
            "      90     -90      6.083    kcal/mol\n",
            "      90     -75      4.257    kcal/mol\n",
            "      90     -60      4.157    kcal/mol\n",
            "      90     -45      5.385    kcal/mol\n",
            "      90     -30      6.754    kcal/mol\n",
            "      90     -15      7.795    kcal/mol\n",
            "      90       0      7.852    kcal/mol\n",
            "      90      15      7.176    kcal/mol\n",
            "      90      30      6.747    kcal/mol\n",
            "      90      45      7.773    kcal/mol\n",
            "      90      60      8.536    kcal/mol\n",
            "      90      75      9.043    kcal/mol\n",
            "      90      90      9.178    kcal/mol\n",
            "      90     105      9.261    kcal/mol\n",
            "      90     120      9.414    kcal/mol\n",
            "      90     135      9.304    kcal/mol\n",
            "      90     150      9.118    kcal/mol\n",
            "      90     165      8.758    kcal/mol\n",
            "      90     180      8.283    kcal/mol\n",
            "     105    -180      8.725    kcal/mol\n",
            "     105    -165      7.928    kcal/mol\n",
            "     105    -150      7.533    kcal/mol\n",
            "     105    -135      8.146    kcal/mol\n",
            "     105    -120      9.041    kcal/mol\n",
            "     105    -105      9.399    kcal/mol\n",
            "     105     -90      8.398    kcal/mol\n",
            "     105     -75      6.919    kcal/mol\n",
            "     105     -60      6.669    kcal/mol\n",
            "     105     -45      7.643    kcal/mol\n",
            "     105     -30      8.277    kcal/mol\n",
            "     105     -15      8.493    kcal/mol\n",
            "     105       0      8.265    kcal/mol\n",
            "     105      15      8.235    kcal/mol\n",
            "     105      30      8.714    kcal/mol\n",
            "     105      45      9.486    kcal/mol\n",
            "     105      60      10.218   kcal/mol\n",
            "     105      75      10.203   kcal/mol\n",
            "     105      90      9.915    kcal/mol\n",
            "     105     105      9.901    kcal/mol\n",
            "     105     120      10.143   kcal/mol\n",
            "     105     135      10.167   kcal/mol\n",
            "     105     150      10.030   kcal/mol\n",
            "     105     165      9.509    kcal/mol\n",
            "     105     180      8.726    kcal/mol\n",
            "     120    -180      8.184    kcal/mol\n",
            "     120    -165      7.473    kcal/mol\n",
            "     120    -150      6.984    kcal/mol\n",
            "     120    -135      7.332    kcal/mol\n",
            "     120    -120      8.433    kcal/mol\n",
            "     120    -105      9.416    kcal/mol\n",
            "     120     -90      9.551    kcal/mol\n",
            "     120     -75      8.974    kcal/mol\n",
            "     120     -60      8.429    kcal/mol\n",
            "     120     -45      8.130    kcal/mol\n",
            "     120     -30      8.574    kcal/mol\n",
            "     120     -15      8.526    kcal/mol\n",
            "     120       0      8.683    kcal/mol\n",
            "     120      15      9.075    kcal/mol\n",
            "     120      30      9.762    kcal/mol\n",
            "     120      45      10.577   kcal/mol\n",
            "     120      60      10.722   kcal/mol\n",
            "     120      75      9.735    kcal/mol\n",
            "     120      90      9.655    kcal/mol\n",
            "     120     105      10.158   kcal/mol\n",
            "     120     120      10.373   kcal/mol\n",
            "     120     135      10.306   kcal/mol\n",
            "     120     150      9.912    kcal/mol\n",
            "     120     165      8.993    kcal/mol\n",
            "     120     180      7.963    kcal/mol\n",
            "     135    -180      6.777    kcal/mol\n",
            "     135    -165      6.474    kcal/mol\n",
            "     135    -150      6.455    kcal/mol\n",
            "     135    -135      6.730    kcal/mol\n",
            "     135    -120      7.565    kcal/mol\n",
            "     135    -105      8.746    kcal/mol\n",
            "     135     -90      9.389    kcal/mol\n",
            "     135     -75      9.264    kcal/mol\n",
            "     135     -60      8.713    kcal/mol\n",
            "     135     -45      8.148    kcal/mol\n",
            "     135     -30      8.363    kcal/mol\n",
            "     135     -15      8.350    kcal/mol\n",
            "     135       0      8.883    kcal/mol\n",
            "     135      15      9.860    kcal/mol\n",
            "     135      30      10.766   kcal/mol\n",
            "     135      45      11.489   kcal/mol\n",
            "     135      60      9.619    kcal/mol\n",
            "     135      75      8.996    kcal/mol\n",
            "     135      90      9.308    kcal/mol\n",
            "     135     105      9.809    kcal/mol\n",
            "     135     120      9.835    kcal/mol\n",
            "     135     135      9.356    kcal/mol\n",
            "     135     150      8.601    kcal/mol\n",
            "     135     165      7.588    kcal/mol\n",
            "     135     180      6.895    kcal/mol\n",
            "     150    -180      5.426    kcal/mol\n",
            "     150    -165      5.662    kcal/mol\n",
            "     150    -150      6.024    kcal/mol\n",
            "     150    -135      6.284    kcal/mol\n",
            "     150    -120      6.995    kcal/mol\n",
            "     150    -105      8.053    kcal/mol\n",
            "     150     -90      8.886    kcal/mol\n",
            "     150     -75      9.093    kcal/mol\n",
            "     150     -60      8.532    kcal/mol\n",
            "     150     -45      7.918    kcal/mol\n",
            "     150     -30      7.969    kcal/mol\n",
            "     150     -15      8.078    kcal/mol\n",
            "     150       0      8.912    kcal/mol\n",
            "     150      15      10.118   kcal/mol\n",
            "     150      30      11.427   kcal/mol\n",
            "     150      45      9.162    kcal/mol\n",
            "     150      60      8.293    kcal/mol\n",
            "     150      75      8.112    kcal/mol\n",
            "     150      90      8.573    kcal/mol\n",
            "     150     105      8.956    kcal/mol\n",
            "     150     120      8.436    kcal/mol\n",
            "     150     135      7.387    kcal/mol\n",
            "     150     150      6.474    kcal/mol\n",
            "     150     165      5.639    kcal/mol\n",
            "     150     180      5.503    kcal/mol\n",
            "     165    -180      3.986    kcal/mol\n",
            "     165    -165      4.647    kcal/mol\n",
            "     165    -150      5.397    kcal/mol\n",
            "     165    -135      5.954    kcal/mol\n",
            "     165    -120      6.733    kcal/mol\n",
            "     165    -105      7.633    kcal/mol\n",
            "     165     -90      8.317    kcal/mol\n",
            "     165     -75      8.439    kcal/mol\n",
            "     165     -60      7.860    kcal/mol\n",
            "     165     -45      7.135    kcal/mol\n",
            "     165     -30      7.027    kcal/mol\n",
            "     165     -15      7.277    kcal/mol\n",
            "     165       0      8.439    kcal/mol\n",
            "     165      15      10.132   kcal/mol\n",
            "     165      30      11.921   kcal/mol\n",
            "     165      45      7.536    kcal/mol\n",
            "     165      60      7.063    kcal/mol\n",
            "     165      75      7.101    kcal/mol\n",
            "     165      90      7.502    kcal/mol\n",
            "     165     105      7.290    kcal/mol\n",
            "     165     120      6.148    kcal/mol\n",
            "     165     135      5.025    kcal/mol\n",
            "     165     150      4.264    kcal/mol\n",
            "     165     165      3.802    kcal/mol\n",
            "     165     180      4.049    kcal/mol\n",
            "     180    -180      2.669    kcal/mol\n",
            "     180    -165      3.473    kcal/mol\n",
            "     180    -150      4.406    kcal/mol\n",
            "     180    -135      5.126    kcal/mol\n",
            "     180    -120      5.965    kcal/mol\n",
            "     180    -105      6.814    kcal/mol\n",
            "     180     -90      7.313    kcal/mol\n",
            "     180     -75      7.241    kcal/mol\n",
            "     180     -60      6.951    kcal/mol\n",
            "     180     -45      6.360    kcal/mol\n",
            "     180     -30      5.783    kcal/mol\n",
            "     180     -15      6.258    kcal/mol\n",
            "     180       0      7.812    kcal/mol\n",
            "     180      15      9.954    kcal/mol\n",
            "     180      30      5.672    kcal/mol\n",
            "     180      45      5.122    kcal/mol\n",
            "     180      60      5.138    kcal/mol\n",
            "     180      75      5.410    kcal/mol\n",
            "     180      90      5.688    kcal/mol\n",
            "     180     105      5.251    kcal/mol\n",
            "     180     120      4.220    kcal/mol\n",
            "     180     135      3.165    kcal/mol\n",
            "     180     150      2.529    kcal/mol\n",
            "     180     165      2.293    kcal/mol\n",
            "     180     180      2.649    kcal/mol\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Check the reference 2D map calculated with TorchANI**:\n",
        "\n",
        "#@markdown X-axis and Y-axis are dihedrals angles degrees and Z-axis is energy in kcal/mol.\n",
        "\n",
        "from scipy.ndimage import gaussian_filter1d\n",
        "from scipy.interpolate import BSpline\n",
        "from scipy.interpolate import griddata\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "with open('ani_plot.dat', 'w') as f:\n",
        "    for item1, item2, item3 in zip(dihedral_degrees1, dihedral_degrees2, ani_energies):\n",
        "      degree_str1 = str(\"{:.0f}\".format(item1))\n",
        "      degree_str2 = str(\"{:.0f}\".format(item2))\n",
        "      energy_str = str(\"{:.3f}\".format(item3))\n",
        "      string = degree_str1.rjust(8, \" \") + degree_str2.rjust(8, \" \") + \"      \" + energy_str.ljust(8, \" \") + \" kcal/mol\"\n",
        "      f.write(\"%s\\n\" % item3)\n",
        "\n",
        "\n",
        "dihedral_degrees1 = []\n",
        "dihedral_degrees2 = []\n",
        "deg_points = range(-180, (180+int(degrees_steps)), int(degrees_steps))\n",
        "\n",
        "for i in range(-180, (180+int(degrees_steps)), int(degrees_steps)):\n",
        "  for deg1, deg2 in zip(np.repeat(i, len(deg_points)), range(-180, (180+int(degrees_steps)), int(degrees_steps))):\n",
        "    dihedral_degrees1.append(deg1)\n",
        "    dihedral_degrees2.append(deg2)\n",
        "\n",
        "data_file = np.loadtxt(os.path.join(workDir, 'ani_plot.dat'))\n",
        "x = dihedral_degrees1\n",
        "y = dihedral_degrees2\n",
        "# z = ani_energies\n",
        "z = data_file\n",
        "max_X = np.amax(x)\n",
        "max_y = np.amax(y)\n",
        "min_x = np.amin(x)\n",
        "min_y = np.amin(y)\n",
        "ycli = np.linspace(max_y, min_y)\n",
        "xcli = np.linspace(min_x, max_X)\n",
        "X, Y = np.meshgrid(xcli, ycli)\n",
        "Z = griddata((x, y), z, (X, Y), method='linear')\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.imshow(Z, extent=[ min_x, max_X, min_y, max_y], cmap=plt.cm.get_cmap('RdBu_r',30), interpolation='bicubic', alpha=1, aspect='auto')\n",
        "\n",
        "plt.xlabel('$\\phi$',  fontsize = 25, fontweight = 'bold')\n",
        "plt.ylabel('$\\psi$', fontsize = 25, fontweight = 'bold')\n",
        "cbar1 = plt.colorbar(orientation=\"vertical\", shrink=0.85, pad=0.05)\n",
        "cbar1.ax.tick_params(labelsize=20)\n",
        "cbar1.set_label('Energy (kcal/mol)',fontsize = 22, fontweight = 'bold')\n",
        "plt.yticks(size=20)\n",
        "plt.xticks(size=20)\n",
        "plt.savefig(os.path.join(workDir, \"ani.png\"), dpi=600)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "cellView": "form",
        "id": "7Wv3AyXqnQy2",
        "outputId": "7ae6035a-e229-4711-9481-c405c3374631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Download your results (If you are running on Google Colab)**\n",
        "from google.colab import files\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding\n",
        "\n",
        "if os.path.exists(\"dihedral_TorchANI2D.zip\"):\n",
        "  os.remove(\"dihedral_TorchANI2D.zip\")\n",
        "else:\n",
        "  pass\n",
        "\n",
        "!zip -FSr \"dihedral_TorchANI2D.zip\" \"mol_files\" \"pdb_min\" *\".pdb\" *\".png\" *.\"txt\" *.\"dat\" *.\"svg\"  >/dev/null 2>&1\n",
        "files.download('dihedral_TorchANI2D.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "cellView": "form",
        "id": "ttoDHMyywGWp",
        "outputId": "56569141-7848-466e-b864-03bdc34bba7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
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