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    "# Molecular Derivatives\n",
    "\n",
    "> ### In this tutorial we will cover:\n",
    "> - how we can automatically generate derivatives of molecules by targeted changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BuildAMol presents first and foremost a framework that allows users to design molecules on their own initiative, by assembling molecular fragments and adjusting the generated structures. However, in many applications, such as drug design, we often do not have a clear idea about the exact molecule wer are interested in. Instead, we may only have a broad idea about the chemical class of molecule we are looking for but the precise nature of its structure. \n",
    "\n",
    "BuildAMol offers an extension named `molecular_factories` which contains pre-implemented classes to help with automated molecule creation. In this tutorial we will be using on factory named the `Derivator` to make \"molecule derivatives\" from a starting compound. \n",
    "\n",
    "The Derivator\n",
    "-------------\n",
    "The `Derivator` is a class that takes in a starting molecule and information on possible modifications to do. Possible modifications include:\n",
    "- changing the element of an atom\n",
    "- changing the order of a bond\n",
    "- adding a functional group onto an atom (or any other function working on a specific atom)\n",
    "- any function that modifies the molecule as a whole\n",
    "\n",
    "Using these the `Derivator` offers methods `make`, `sample`, and `all` to generate derivative molecules that contain applied modifications.\n",
    "- `make` allows the user to make a specific derivative based on the potential changes that were specified\n",
    "- `sample` allows the user to create one or more random derivatives in a stochastic way\n",
    "- `all` allows the user to generate all combinatorically possible molecule derivatives\n",
    "\n",
    "So, with all that said, let's dive into an example!"
   ]
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   "source": [
    "## Example 1\n",
    "\n",
    "As a first try, let's start with a benzene ring and modify the following:\n",
    "- atom `C1` could be a `C`, `N`, or `O`\n",
    "- atom `C3` could be a `C`, or `N`\n",
    "- we could have a `carboxyl` or an `amide` group at atom `C4`\n",
    "\n",
    "So, how do we set up this kind of simulation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import buildamol as bam\n",
    "from buildamol.extensions.molecular_factories import Derivator\n",
    "\n",
    "# create the benzene starting molecule\n",
    "bam.load_small_molecules()\n",
    "benzene = bam.get_compound(\"benzene\")\n",
    "\n",
    "# create the derivator object\n",
    "derivator = Derivator(benzene)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our desired modifications like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<buildamol.extensions.molecular_factories.derivator.Derivator at 0x13558cc10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can give any atom identifier as \"atom\" argument, or the an atom object\n",
    "derivator.element_changable(atom=\"C1\", elements=(\"C\", \"N\", \"O\"))\n",
    "derivator.element_changable(atom=\"C3\", elements=(\"C\", \"N\"))\n",
    "\n",
    "derivator.functional_group_addable(atom=\"C4\", group_modifiers=(bam.carboxylate, bam.amidate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And with that done we can try to sample a possible molecule like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    }
   ],
   "source": [
    "# sample is a generator that will yield molecules\n",
    "mol = next(derivator.sample())\n",
    "mol.py3dmol().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool! So we have a molecule with some changes applied. Since we actually don't have that many changes we can also use `all` to compute all possible molecules from the changes we specified. Here's how:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of possible molecules: 10\n"
     ]
    }
   ],
   "source": [
    "# make a list of all possible molecules\n",
    "# all is a generator that will yield molecules\n",
    "mols = list(derivator.all())\n",
    "print(f\"Number of possible molecules: {len(mols)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's visualize the molecules\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# make a nice grid of subplots\n",
    "rows = np.sqrt(len(mols))   \n",
    "cols = np.ceil(len(mols) / rows)\n",
    "rows = np.ceil(rows)\n",
    "fig, axs = plt.subplots(int(rows), int(cols), figsize=(10, 10))\n",
    "\n",
    "for i, mol in enumerate(mols):\n",
    "    ax = axs[int(i // cols), int(i % cols)]\n",
    "    ax.imshow(mol.chem2dview().draw())\n",
    "for ax in axs.flat:    \n",
    "    ax.axis(\"off\")"
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    "And there we have all our possible molecules that fulfill our specifications! \n",
    "\n",
    "## Example 2\n",
    "\n",
    "We can also take this a step further and make a more elaborate pipeline, where we have more options and therefore a larger molecule space. If we have a large number of possible combinations it can get exhaustive to compute all possibilities so we may want to rely on `sample` or `make` instead. We can specify _probabilities_ for specific modifications to guide how frequently they are chosen by the sampling algorithm and thereby make sure that \"less likely\" alterations are not given too much attention. "
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    "To illustrate this, let's make a second example where we add a wider variety of possible modifications! Also, let's add a custom global modifier that will circularize our molecule targets with either 6-membered or 5-membered rings!"
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      "/Users/noahhk/anaconda3/envs/glyco2/lib/python3.11/site-packages/plotly/express/_core.py:1985: FutureWarning:\n",
      "\n",
      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
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            "gridwidth": 2,
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            "showline": true,
            "ticks": "outside",
            "zeroline": false,
            "zerolinecolor": "rgb(36,36,36)"
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          },
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           "opacity": 0.3
          },
          "ternary": {
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            "gridcolor": "rgb(232,232,232)",
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            "showline": true,
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           "baxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "rgb(232,232,232)",
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          },
          "title": {
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   ],
   "source": [
    "# let's start from a di-benzene ring\n",
    "start = bam.read_smiles(\"C1(C=CC=C2)=C2C=CC=C1\")\n",
    "start.autolabel()\n",
    "# so that we know which atom is where\n",
    "start.plotly().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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   "source": [
    "\n",
    "derivator = Derivator(start)\n",
    "\n",
    "# let's add some modifiers\n",
    "# (this time we specify the probability for each element)\n",
    "derivator\\\n",
    "    .element_changable(\"C10\", (\"C\", \"N\", \"O\"), probabilities=(0.5, 0.3, 0.2))\\\n",
    "    .element_changable(\"C8\", (\"C\", \"N\"), probabilities=(0.6, 0.4))\\\n",
    "    .element_changable(\"C6\", (\"C\", \"N\", \"S\"), probabilities=(0.5, 0.3, 0.2))\n",
    "\n",
    "derivator\\\n",
    "    .functional_group_addable(\"C5\", (bam.carboxylate, bam.amidate), probabilities=(0.5, 0.5))\\\n",
    "    .functional_group_addable(\"C7\", (bam.hydroxylate, bam.aminate, bam.thiolate), probabilities=(0.5, 0.4, 0.1))\\\n",
    "    .functional_group_addable(\"C9\", (bam.aminate, derivator.nothing), probabilities=(0.3, 0.7))\n",
    "\n",
    "next(derivator.sample()).py3dmol().show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's define a modifier function that will circularize the molecule at the carboxyl or amide group at atom C5, adding a third ring. However, we want that ring to have either six or five member atoms, so we will dynamically one or two more carbon atoms onto atom C4. Sounds a bit complex? Well, maybe but let's see what our function looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "<div id=\"3dmolviewer_17242329103482969\"  style=\"position: relative; width: 600px; height: 500px;\">\n",
       "        <p id=\"3dmolwarning_17242329103482969\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: <br>\n",
       "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
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       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.0.3/3Dmol-min.js');\n",
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       "viewer_17242329103482969.zoomTo();\n",
       "\tviewer_17242329103482969.addModel(\"HETATM    1 C1   UNL     1      -0.324   0.241  -0.961  1.00  0.00          C   \\nHETATM    2 C6   UNL     1       0.043  -0.848  -1.737  1.00  0.00          C   \\nHETATM    3 C5   UNL     1       0.964  -1.796  -1.282  1.00  0.00          C   \\nHETATM    4 C4   UNL     1       1.543  -1.654  -0.021  1.00  0.00          C   \\nHETATM    5 C3   UNL     1       1.193  -0.556   0.771  1.00  0.00          C   \\nHETATM    6 C2   UNL     1       0.244   0.396   0.324  1.00  0.00          C   \\nHETATM    7 C7   UNL     1      -0.175   1.512   1.087  1.00  0.00          C   \\nHETATM    8 C8   UNL     1      -1.023   2.447   0.493  1.00  0.00          C   \\nHETATM    9 C9   UNL     1      -1.527   2.197  -0.770  1.00  0.00          C   \\nHETATM   10 N10  UNL     1      -1.200   1.112  -1.483  1.00  0.00          N   \\nHETATM   11 H6   UNL     1      -0.393  -0.952  -2.729  1.00  0.00          H   \\nHETATM   12 CA   UNL     1       2.566  -2.654   0.427  1.00  0.00          C   \\nHETATM   13 H3   UNL     1       1.674  -0.434   1.738  1.00  0.00          H   \\nHETATM   14 H8   UNL     1      -1.290   3.363   1.006  1.00  0.00          H   \\nHETATM   15 CB   UNL     1       2.202  -3.998  -0.177  1.00  0.00          C   \\nHETATM   16 H42  UNL     1       3.550  -2.326   0.072  1.00  0.00          H   \\nHETATM   17 H43  UNL     1       2.594  -2.733   1.519  1.00  0.00          H   \\nHETATM   18 H411 UNL     1       2.978  -4.735   0.054  1.00  0.00          H   \\nHETATM   19 H412 UNL     1       1.252  -4.383   0.213  1.00  0.00          H   \\nHETATM   20 C    CBX     2       1.353  -2.924  -2.172  1.00  0.00          C   \\nHETATM   21 O    CBX     2       1.030  -2.947  -3.351  1.00  0.00          O   \\nHETATM   22 OXT  CBX     2       2.101  -3.910  -1.609  1.00  0.00          O   \\nHETATM   23 N    NH3     3       0.389   1.822   2.343  1.00  0.00          N   \\nHETATM   24 HN2  NH3     3      -0.157   2.486   2.885  1.00  0.00          H   \\nHETATM   25 HN3  NH3     3       0.641   1.011   2.897  1.00  0.00          H   \\nHETATM   26 N    NH3     4      -2.387   3.105  -1.384  1.00  0.00          N   \\nHETATM   27 HN2  NH3     4      -2.652   2.701  -2.279  1.00  0.00          H   \\nHETATM   28 HN3  NH3     4      -3.195   3.307  -0.808  1.00  0.00          H   \\nCONECT    1    2    2    6   10\\nCONECT    2    1    1    3   11\\nCONECT    6    1    5    5    7\\nCONECT   10    1    9    9\\nCONECT    3    2    4    4   20\\nCONECT   11    2\\nCONECT    4    3    3    5   12\\nCONECT    5    4    6    6   13\\nCONECT   12    4   15   16   17\\nCONECT   13    5\\nCONECT    7    6    8    8   23\\nCONECT    8    7    7    9   14\\nCONECT    9    8   10   10   26\\nCONECT   14    8\\nCONECT   20   21   21   22    3\\nCONECT   21   20   20\\nCONECT   22   20   15\\nCONECT   23   24   25    7\\nCONECT   24   23\\nCONECT   25   23\\nCONECT   26   27   28    9\\nCONECT   27   26\\nCONECT   28   26\\nCONECT   15   12   18   19   22\\nCONECT   16   12\\nCONECT   17   12\\nCONECT   18   15\\nCONECT   19   15\\nEND\",\"pdb\");\n",
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    }
   ],
   "source": [
    "\n",
    "# and now let's also define a custom global modifier that will circularize part of the molecule\n",
    "# with either 5 or 6 atoms\n",
    "\n",
    "def circularize_at_c5_branch(mol):\n",
    "    \n",
    "    # the functional group at C5 will be residue 2 (since it's the first functional group we add)\n",
    "    # we will get the N or O neighbor of the carbonyl carbon and connect it to the aromatic ring by adding\n",
    "    # either one or two carbons at the C4 position\n",
    "    neighbor = mol.get_neighbors(\n",
    "        mol.get_residue(2).get_atom(\"C\"),\n",
    "        filter = lambda x: x.element in (\"N\", \"O\") and x.id != \"O\"\n",
    "    )\n",
    "    neighbor = neighbor.pop()\n",
    "\n",
    "    # add one or two carbons at C4\n",
    "    # (with a 50% probability for either case)\n",
    "    atom = mol.get_hydrogen(\"C4\")\n",
    "    mol.change_element(atom, \"C\").rename_atom(atom, \"CA\")\n",
    "    if np.random.rand() < 0.5:\n",
    "        atom = mol.get_hydrogen(atom)\n",
    "        mol.change_element(atom, \"C\").rename_atom(atom, \"CB\")\n",
    "\n",
    "    # add a bond from the neighbor to the new carbon\n",
    "    mol.add_bond(neighbor, atom)\n",
    "    Ha = mol.get_hydrogen(atom)\n",
    "    if Ha is not None:\n",
    "        mol.remove_atoms(Ha)\n",
    "    Hn = mol.get_hydrogen(neighbor)\n",
    "    if Hn is not None:\n",
    "        mol.remove_atoms(Hn)\n",
    "        \n",
    "    # since we added a circle we need to optimize with rdkit\n",
    "    mol.optimize(algorithm=\"mmff\", inplace=True)\n",
    "\n",
    "# now add the modifier\n",
    "derivator.global_modifiers([circularize_at_c5_branch])\n",
    "\n",
    "# and sample a molecule\n",
    "next(derivator.sample()).py3dmol().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okey, let's try to sample some molecules using our set up derivator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(5, 5, figsize=(10, 10))\n",
    "\n",
    "# create 25 molecules\n",
    "for i, mol in enumerate(derivator.sample(25)):\n",
    "    ax = axs.flat[i]\n",
    "    ax.imshow(mol.chem2dview().draw())\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there we have a bunch of sampled molecule derivatives!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3\n",
    "\n",
    "So far we have seen `all` and `sample` in action. Then, what is the purpose of `make`? The `make` method accepts numeric inputs in form of arrays to specify what kinds of modifications to specifically apply when generating derivatives. It is designed to facilitate the use of the `Derivator` in machine learning or optimization pipelines, which work with numeric data. \n",
    "\n",
    "Let's make an example to understand how we use the `make` method. In summary, the method accepts four 1D arrays, each of the length of specified atoms/bonds/modifiers. Each entry in the arrays is the index of the respective element/bond order/modifier to apply. Of course, a user is free to design their own numeric encoding/decoding for the Derivator if they find another system more convenient for their pipeline! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's use again benzene\n",
    "benzene = bam.get_compound(\"benzene\")\n",
    "\n",
    "derivator = Derivator(benzene)\n",
    "\n",
    "# define potential modifications\n",
    "# (we keep the order of specifications in mind, for later)\n",
    "derivator.element_changable(\"C1\", (\"C\", \"N\", \"O\"))\n",
    "derivator.element_changable(\"C3\", (\"C\", \"N\"))\n",
    "derivator.functional_group_addable(\"C4\", (bam.carboxylate, bam.amidate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To now make a \"benzene with C1 as Oxygen and C3 as Nitrogen, and a carboxyl group at C4\" we would specify the following arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "viewer_1724232938918048.zoomTo();\n",
       "\tviewer_1724232938918048.addModel(\"HETATM    1 O1   BNZ A   1       0.608  -1.172   0.000  1.00  0.00          O 1+\\nHETATM    2 C2   BNZ A   1      -0.732  -1.230   0.000  1.00  0.00          C   \\nHETATM    3 N3   BNZ A   1      -1.449   0.053   0.000  1.00  0.00          N   \\nHETATM    4 C4   BNZ A   1      -0.742   1.240   0.000  1.00  0.00          C   \\nHETATM    5 C5   BNZ A   1       0.640   1.222   0.001  1.00  0.00          C   \\nHETATM    6 C6   BNZ A   1       1.339   0.057  -0.001  1.00  0.00          C   \\nHETATM    7 H2   BNZ A   1      -1.327  -2.081   0.000  1.00  0.00          H   \\nHETATM    8 H5   BNZ A   1       1.193   2.149   0.000  1.00  0.00          H   \\nHETATM    9 H6   BNZ A   1       2.419   0.042  -0.001  1.00  0.00          H   \\nHETATM   10 C    CBX A   2      -1.494   2.585  -0.001  1.00  0.00          C   \\nHETATM   11 O    CBX A   2      -1.667   3.179  -1.039  1.00  0.00          O   \\nHETATM   12 OXT  CBX A   2      -1.956   3.096   1.150  1.00  0.00          O   \\nHETATM   13 HXT  CBX A   2      -1.818   2.620   1.981  1.00  0.00          H   \\nCONECT    1    2    2    6\\nCONECT    2    1    1    3    7\\nCONECT    6    1    5    5    9\\nCONECT    3    2    4    4\\nCONECT    7    2\\nCONECT    4    3    3    5   10\\nCONECT    5    4    6    6    8\\nCONECT    8    5\\nCONECT    9    6\\nCONECT   10   11   11   12    4\\nCONECT   11   10   10\\nCONECT   12   10   13\\nCONECT   13   12\\nEND\",\"pdb\");\n",
       "\tviewer_1724232938918048.setStyle({\"stick\": {}});\n",
       "\tviewer_1724232938918048.zoomTo();\n",
       "\tviewer_1724232938918048.setStyle({\"stick\": {}});\n",
       "viewer_1724232938918048.render();\n",
       "});\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the first entry is for C1 (the first specified element_changable)\n",
    "# the second entry is for C3 (the second specified element_changable)\n",
    "# the 2 indicates the index of O in the tuple (\"C\", \"N\", \"O\")\n",
    "# the 1 indicates the index of N in the tuple (\"C\", \"N\")\n",
    "elements = [2, 1]\n",
    "\n",
    "# the first entry is for C4 (the first and only specified functional_group_addable)\n",
    "# the 0 indicates the index of carboxylate in the tuple (bam.carboxylate, bam.amidate)\n",
    "functional_groups = [0]\n",
    "\n",
    "# now we can make the derivative\n",
    "# (we have to provide empty arrays for all other arguments)\n",
    "derivator.make(elements=elements, bonds=[], groups=functional_groups, modifiers=[]).py3dmol().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And with that we have reached the end of this tutorial. We saw how we can generate molecular derivatives from a starting compound and a bunch of specified potential modifications using the `Derivator` class. Thanks for checking out this tutorial and good luck with your project using BuildAMol!"
   ]
  }
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