Forward_Transformer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7ad69c9-bf27-4594-982a-0a2a42749808",
   "metadata": {},
   "source": [
    "# MoleculeDiffusionTransformer: Forward transformer model (predict molecular properties from SMILES) \n",
    "\n",
    "Reference:\n",
    "\n",
    "#### Generative Discovery of Novel Chemical Designs using Diffusion Modeling and Transformer Architectures with Application to Deep Eutectic Solvents \n",
    "\n",
    "Rachel K. Luu1,2, Marcin Wysokowski1 , Markus J. Buehler1,3*\n",
    "\n",
    "1 Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA\n",
    "2 Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA\n",
    "3 Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA\n",
    "\n",
    "mbuehler@MIT.EDU "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a6191d6-930d-4a20-94a0-87f97b5a5a7b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os,sys\n",
    "import math\n",
    "import matplotlib.pyplot as plt \n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "def exists(val):\n",
    "    return val is not None\n",
    "\n",
    "import torch\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "#from tqdm import tqdm, trange\n",
    "from tqdm.notebook import trange, tqdm\n",
    "\n",
    "import torchvision\n",
    " \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "from torch import nn\n",
    "from torch import optim, Tensor\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms, models\n",
    "import numpy as np\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import ExponentialLR, StepLR\n",
    "from functools import partial, wraps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7cb6949-e793-4614-a24c-f36a0194a353",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from einops import rearrange, repeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "475ea20b-5326-41f6-9f3c-e11e863bf67f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() \n",
    "                                  else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69514f21-e80f-44d4-aade-05e7c6c63621",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "423f3887-7c50-4a95-b0ac-6ff6e3d4d5c6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-08 20:14:11.256087: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-08 20:14:11.401516: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "from MoleculeDiffusion import draw_and_save, draw_and_save_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8a2ef7c-e0b4-404b-acc1-d7ad4cf46c86",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch version: 2.0.0+cu117\n"
     ]
    }
   ],
   "source": [
    "print(\"Torch version:\", torch.__version__) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fe243fd-aa64-4eb8-b24f-921587c38a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#QM9: https://github.com/wengong-jin/chemprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef0479bb-900f-4f34-b641-536e0ea1b9d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mu</th>\n",
       "      <th>alpha</th>\n",
       "      <th>homo</th>\n",
       "      <th>lumo</th>\n",
       "      <th>gap</th>\n",
       "      <th>r2</th>\n",
       "      <th>zpve</th>\n",
       "      <th>cv</th>\n",
       "      <th>u0</th>\n",
       "      <th>u298</th>\n",
       "      <th>h298</th>\n",
       "      <th>g298</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "      <td>133885.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.706037</td>\n",
       "      <td>75.191296</td>\n",
       "      <td>-0.239977</td>\n",
       "      <td>0.011124</td>\n",
       "      <td>0.251100</td>\n",
       "      <td>1189.527450</td>\n",
       "      <td>0.148524</td>\n",
       "      <td>31.600676</td>\n",
       "      <td>-411.543985</td>\n",
       "      <td>-411.535513</td>\n",
       "      <td>-411.534569</td>\n",
       "      <td>-411.577397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.530394</td>\n",
       "      <td>8.187793</td>\n",
       "      <td>0.022131</td>\n",
       "      <td>0.046936</td>\n",
       "      <td>0.047519</td>\n",
       "      <td>279.757172</td>\n",
       "      <td>0.033274</td>\n",
       "      <td>4.062471</td>\n",
       "      <td>40.060230</td>\n",
       "      <td>40.060012</td>\n",
       "      <td>40.060012</td>\n",
       "      <td>40.060741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.310000</td>\n",
       "      <td>-0.428600</td>\n",
       "      <td>-0.175000</td>\n",
       "      <td>0.024600</td>\n",
       "      <td>19.000200</td>\n",
       "      <td>0.015951</td>\n",
       "      <td>6.002000</td>\n",
       "      <td>-714.568061</td>\n",
       "      <td>-714.560153</td>\n",
       "      <td>-714.559209</td>\n",
       "      <td>-714.602138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.588700</td>\n",
       "      <td>70.380000</td>\n",
       "      <td>-0.252500</td>\n",
       "      <td>-0.023800</td>\n",
       "      <td>0.216300</td>\n",
       "      <td>1018.322600</td>\n",
       "      <td>0.125289</td>\n",
       "      <td>28.942000</td>\n",
       "      <td>-437.913936</td>\n",
       "      <td>-437.905942</td>\n",
       "      <td>-437.904997</td>\n",
       "      <td>-437.947682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>-0.241000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.249400</td>\n",
       "      <td>1147.585800</td>\n",
       "      <td>0.148329</td>\n",
       "      <td>31.555000</td>\n",
       "      <td>-417.864758</td>\n",
       "      <td>-417.857351</td>\n",
       "      <td>-417.856407</td>\n",
       "      <td>-417.895731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.636100</td>\n",
       "      <td>80.520000</td>\n",
       "      <td>-0.228700</td>\n",
       "      <td>0.049200</td>\n",
       "      <td>0.288200</td>\n",
       "      <td>1308.816600</td>\n",
       "      <td>0.171150</td>\n",
       "      <td>34.276000</td>\n",
       "      <td>-387.049166</td>\n",
       "      <td>-387.039746</td>\n",
       "      <td>-387.038802</td>\n",
       "      <td>-387.083279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.556400</td>\n",
       "      <td>196.620000</td>\n",
       "      <td>-0.101700</td>\n",
       "      <td>0.193500</td>\n",
       "      <td>0.622100</td>\n",
       "      <td>3374.753200</td>\n",
       "      <td>0.273944</td>\n",
       "      <td>46.969000</td>\n",
       "      <td>-40.478930</td>\n",
       "      <td>-40.476062</td>\n",
       "      <td>-40.475117</td>\n",
       "      <td>-40.498597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mu          alpha           homo           lumo  \\\n",
       "count  133885.000000  133885.000000  133885.000000  133885.000000   \n",
       "mean        2.706037      75.191296      -0.239977       0.011124   \n",
       "std         1.530394       8.187793       0.022131       0.046936   \n",
       "min         0.000000       6.310000      -0.428600      -0.175000   \n",
       "25%         1.588700      70.380000      -0.252500      -0.023800   \n",
       "50%         2.500000      75.500000      -0.241000       0.012000   \n",
       "75%         3.636100      80.520000      -0.228700       0.049200   \n",
       "max        29.556400     196.620000      -0.101700       0.193500   \n",
       "\n",
       "                 gap             r2           zpve             cv  \\\n",
       "count  133885.000000  133885.000000  133885.000000  133885.000000   \n",
       "mean        0.251100    1189.527450       0.148524      31.600676   \n",
       "std         0.047519     279.757172       0.033274       4.062471   \n",
       "min         0.024600      19.000200       0.015951       6.002000   \n",
       "25%         0.216300    1018.322600       0.125289      28.942000   \n",
       "50%         0.249400    1147.585800       0.148329      31.555000   \n",
       "75%         0.288200    1308.816600       0.171150      34.276000   \n",
       "max         0.622100    3374.753200       0.273944      46.969000   \n",
       "\n",
       "                  u0           u298           h298           g298  \n",
       "count  133885.000000  133885.000000  133885.000000  133885.000000  \n",
       "mean     -411.543985    -411.535513    -411.534569    -411.577397  \n",
       "std        40.060230      40.060012      40.060012      40.060741  \n",
       "min      -714.568061    -714.560153    -714.559209    -714.602138  \n",
       "25%      -437.913936    -437.905942    -437.904997    -437.947682  \n",
       "50%      -417.864758    -417.857351    -417.856407    -417.895731  \n",
       "75%      -387.049166    -387.039746    -387.038802    -387.083279  \n",
       "max       -40.478930     -40.476062     -40.475117     -40.498597  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"qm9_.csv\")\n",
    " \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35661a6f-51cd-4a42-8b8a-6f99ab7658db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                           C\n",
       "1                           N\n",
       "2                           O\n",
       "3                         C#C\n",
       "4                         C#N\n",
       "                 ...         \n",
       "133880    C1C2C3C4C5OC14C5N23\n",
       "133881    C1N2C3C2C2C4OC12C34\n",
       "133882    C1N2C3C4C5C2C13CN45\n",
       "133883    C1N2C3C4C5CC13C2C45\n",
       "133884    C1N2C3C4C5OC13C2C45\n",
       "Name: smiles, Length: 133885, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['smiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1820475-ef1b-4b8e-954f-62196ca1e845",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         13.21\n",
       "1          9.46\n",
       "2          6.31\n",
       "3         16.28\n",
       "4         12.99\n",
       "          ...  \n",
       "133880    69.37\n",
       "133881    69.52\n",
       "133882    73.60\n",
       "133883    77.40\n",
       "133884    69.48\n",
       "Name: alpha, Length: 133885, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['alpha']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67e9880a-09fb-455c-bb6f-426a775875ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smiles\n",
      "mu\n",
      "alpha\n",
      "homo\n",
      "lumo\n",
      "gap\n",
      "r2\n",
      "zpve\n",
      "cv\n",
      "u0\n",
      "u298\n",
      "h298\n",
      "g298\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bac86890-f9f5-4cc4-997d-e5700baa5cd3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12,), (133885, 13))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0,:] [1:].shape, df.iloc[:,:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd7a6903-e99f-4f2e-996d-b590867b8ba8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133885, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data=df.iloc [:, 1: ] .to_numpy()\n",
    "y_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03bb4c5a-85dc-460b-9bb1-7863468d4ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# labels for y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac23a877-4f7b-4b84-84b0-afae5458903b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'cv', 'u0', 'u298',\n",
       "       'h298', 'g298'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names=df.columns[1:]\n",
    "col_names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a94c16f4-8db4-4ae6-baf9-f000f3609099",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'alpha'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404aaab2-f334-4b9b-b565-d186d28f3c4a",
   "metadata": {},
   "source": [
    "### Smiles to Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "374af80c-15e8-4820-84a8-ce6698727947",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install rdkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "abc9316c-1e9a-489f-bb78-bbbbfb75cafe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "import matplotlib.pyplot as plt\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import rdFMCS\n",
    "from rdkit.Chem.Draw import rdDepictor\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "\n",
    "rdDepictor.SetPreferCoordGen(True)\n",
    "IPythonConsole.drawOptions.minFontSize=20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe866fab-c346-4b4e-afa3-0defc89aa488",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_and_save_set (smiles = ['CC(=CC(C)(C)CCCCCCCC(=O)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C',\n",
    "                                 'CCCC(C)'],\n",
    "                   fname='out.png',\n",
    "                    plot_title=None,\n",
    "                  figsize=1,\n",
    "                       mols_per_row=2,\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f83dbe7-819c-4cce-ad1b-8297141fc7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecule1 = Chem.MolFromSmiles('CC=C')\n",
    "molecule2 = Chem.MolFromSmiles('CNC=C')\n",
    "Draw.MolsToGridImage((molecule1,molecule2), subImgSize=(250,250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b811d811-f98c-49be-820a-d95c424b9125",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_and_save (smi = 'CC=C', GTsmile = 'CNC=C', fname='out.png', add_Hs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7d073cc-02bf-497a-bef8-448ccf81f60d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolsToGridImage((molecule1,molecule2), subImgSize=(250,250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a2cb3a1-488a-402b-8748-5352683dcf86",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wnzghYm0t4uwscvt2gWVkHzF49KdHjx4mr7RnZGRIVFSU2NvbG/sZNGiQJCcnm9SPiMipU6eke/fuxn68vLxk3bp1JteUmZkp8+bNE2dnZ2NfQ4cOlevXr5tcU0JCgvFIBADx9PSUn376Kd/2aWlpMm7cOLG1tRUA0rt3bzl//rwYDAZZtWqVuLm5Gft6++235datWybXZDAY5IMPPjD2U7ly5SdyhGTOnDkCQNauXVvsvqhwpRMed+9qoWBnJ1LYX7aXX9b2XbQoz6evxcfL4MGDRafTiYWFhQwaNEgmTZokTk5OAkDat28vR44cKfRtGAwG+eGHH6RevXoCQGrXri2zZs2STp06CQBxcnKSTz75RB48eFBoXzdv3pS33npLLC0tjZ98H3zwgbGmzp07y/HjxwvtR0Rk27Zt0rRpUwEg1apVk6lTp0pAQIAAEBcXF5k9e7Y8fPiw0H7u3Lkj77//vjEEOnfuLMHBwcaQDAoKktOnTxvb6/V6Wbp0qVSrVk0ASLNmzWT79u0iIhIbGytt2rQRAOLq6iqvvvqqPPfcc8Z/+AsWLJCsrKwivT8RkbNnz8qoUaOMf14WFhZy9erVIu+fn3bt2om9vb3cu3ev2H1R4dTDo25dkYkT8/4JD88ZHjt3ao+Lch7GwoVa20GDcm7PyJATAwfKbUA6AtKpU6ccQ93r16/L0KFDjaFS0Cf1yZMnpVu3bgJAHBwc5MMPPzROMQwGg6xbt07q1KkjAMTb21s2btyY5ydj9gihUqVKAkD8/f1l//79xuevXr0qgwYNMtY0YsQI+eOPP/Ks6fz589KrVy8BILa2tjJ+/HjjERKDwSDffvut1KxZUwCIj4+P/Pzzz3n2o9frZdmyZcYQaNq0qWzbts34/OXLl+WVV14RAGJlZSXvvPOO3Lp1S+Lj48XS0lIqV64sX3zxhWRlZcmVK1dk4MCBAkAsLS1l1KhRxvqzX6dq1aoCQJ577jnZuXNnnjVlv4ddu3ZJz549RafTCQB5/vnnpXXr1gJAWrRoIWlpafnuX5jk5GTR6XTSp08f5T7INOrhUZSf7PBYvVp7XJTDZz//rLUNCtIeGwwiGzeKeHuLAHLR2lp2T5yY7zD3yJEj0r59+zw/qW/cuCFvvvmm8RNvwIABkpSUlGc/6enpMn36dHFwcBAA0q1bNzl58qTx+UdHCJ6enrJ8+fJchyCzHTx4MMcn99y5c401paWlydixY8XGxkYASJ8+feT333/Ps5/79+/LlClTpEKFCgJAgoODcxze3Ldvn/j5+QkAqVSpksyfP18yMzPz7CsmJkZ8fX0FgLi7u8vChQtlzZo1cuvWLeN7d3R0FADywgsvyIkTJ/LsJ3uak13/Sy+9JImJicbnMzIyZOXKlcZ1KZ1OJyEhIbJjxw7j/8OJEycKAOnatatkZGTk+TqFyZ7G/ve//1Xan0xXOtMW1fA4cULkhRe0xw4OIjNmiOH+/UK7MBgMsmbNGqlVq5bxk3rEiBHG+frjI4SCJCUlyYABA4yfvgMHDpTg4OA8RwiF1bRq1SqpUaOGAJBGjRpJZGSk8ZO7WbNmsmPHjiLVdPHiRQkLCxMAYm1tLcOGDZN+/foZRxOjR4+WmzdvFtqPXq+XJUuWiIeHhwCQ5s2by7Rp03KMujZs2FCk9YhHR052dnYyfPhwGT16tHh6egoAsbe3lxEjRuR5LofBYJChQ4caz8/IL4QL0rlzZ7G1tS3W6IVMUzrhoTJtefVVEScn7b8HDhS5csXkUu/fvy9Tp04Va2trASBubm6yYsUKpb+c+/fvl5YtWxoX+QoaIRTk7t27MmHCBLGysjKurZi6ZpBtz549xrWHvNYxiio1NVXGjBljHJFVqFBBZs6cWaT1nsdFR0dLo0aNcizQzpgxQ27cuFHgfllZWcbweeutt0xaQE1JSRFLS0sJCQkxuV5SV/oLpoUcRcmxYLp4sUhsrMklPi42NlZat26tdGTgUXq9XsLCwmTq1KnFrmnfvn3SokULuXbtWrFreumll4p1mDrbL7/8Ij4+Pkqh+KjMzExxdHQUZ2dnk6Yh6enpxsXhjz76qMC2Dx8+lF9//VXmz58v9evXFwASFRVVrLrJNKV3qDYsTNv2r3/lv9+pUyI2NtqIo5j/0Mm8AgMDxd7e3uRDsLdu3TKuJS1evFhEtGlNQkKCrF69Wt555x1p27atcd3n0Z9GjRrJ3bt3S+LtUB5KLzwSE0VcXUUsLfM+DBsfL1KvXpFPEqOn26BBgwRAkdZeHpeUlCReXl4CQOrVqydVqlTJERIuLi7StWtXGT9+vHzzzTeSnJwsY8aMEQDy4osvFulQNhVf6YWHiMi+fSJVqmjPNWsm8uabImPHivTsqY04dDqRSZNMLomePhMmTBAA8ttvvynt//XXXxvDokWLFjJixAhZtmyZnD592rhmFRcXJ15eXrJw4UIxGAzGw8rh4eG8LL8UlO795/7+d+1LqOfOBTZuBJYu1a5xqVoV6N8fePNNoGXLUi2JSkb27Q+TkpLQpEkTk/Y1GAyYM2cOLCwssHv3brTJ/oLzx1SsWBHW1taIiIhAlSpVsHDhQly/fh3Lli2Dp6cnpk+fXuz3QQUwd3rRs+nHH38UAPLll1+avO+SJUuMp78X5uzZs+Lu7i52dnayZ88euXv3rvj7+wsAmTt3rkmve+3aNUlISDC53vKK4UEl4ujRowJAJk+ebNJ+qamp4uHhIe7u7kU+Onbw4EFxcHAQV1dXOXHihKSkpEiDBg1Ep9Ple1GhwWCQS5cuydq1a2Xs2LESEBAgOp1OKlWqpHSIujxieFCJSElJEQAyZMgQk/b7v//7PwEgCxcuNGm/zZs3i5WVldSoUUMuXbokCQkJUq1aNbGxsZHt27fLrVu3ZOvWrfLhhx9Kz549jafvZ//Y29sbt6meqFbeMDyoRBgMBrG1tZUXX3yxyPucPXtWbGxs5Pnnn1c6aW7lypUCQBo3biw3b96UY8eOSYUKFcTCwiJHUFhaWkrz5s1l2LBh8tVXX8mRI0dk7969kpWVZbwNwejRo7noWgiGB5WYypUri6enp+zfv79IU4EePXoIANm1a5fya0ZFRQkAadeundy/f18+/vhjsbS0lK5du8rs2bMlJiYm11W3w4YNExsbG9m2bVuOE9VmzpypXEd5wPCgEpN9USH+vAanVatWMnLkSFm+fLnExcXlmBps3rxZAEi/fv2K/brvvvuuANp9PQwGQ6GX6B8/flxcXFzEyclJjhw5Ijdv3pTGjRsLoH6v1xKxfbtIv34iNWpopza4umqXfHz6qUheN57q0EE7LaKge9PUqiVia6tUDsODSsy+fftkyZIlMnnyZAkODpbKlSvnmD5UrFhRXnjhBRk7dqzUqFFD7Ozs5MKFC8V+Xb1eX+SLDLPt2LFDbGxspGrVqpKQkCAXL16UGjVqiJWVlWzZsqXI/Vy/fl0iIyPlisK1WPnKzBQZOvSvC0T79tXOjxo+/K+779WvL3L2bM79GB70rDAYDPL777/L6tWr5e2335a2bduKnZ2dMUx69uxp1vq+/fZb0el00qBBA0lJSZETJ05IxYoVxdHRUQ4fPpyr/b1792TPnj3y6aefSv/+/aVu3brG99K1a9cnt2YSGamFQKtWIpcv53wuK0tkwoS/7rGTmvrXcwwPepY9fPhQYmJiJDQ0VOLj481djsydO9d424a7d+/KL7/8Ira2tuLm5ibff/+9LFq0SIYMGSLPPfec8Srk7B8fHx8JCwszXqj3JC6glDNntPv+urmJFHS3tbyuHWN4EJWusWPHCgD5xz/+IZmZmbJ27dpcF+FVr15dQkNDZfr06RIdHZ3jnJTU1FR5/vnnBYAsyud2mkX2/vtaAIwdW3C706e1dtWr/7WthMPD/F+PTvSUmT59Oq5cuYLly5cjIiICM2bMgE6ng6urK7788kv4+fkZT7/Pi7OzM37++We0adMGw4YNQ5UqVdCjRw+1Yvbu1X536VJwOx8fwNMTSEoCLl0CvLz+ei4qCnB0zHu/27fV6gJ4ejpRXh4+fCiBgYHSrVs3mT9/vgCQZcuWmdRHfHx8jlPnlfj4aKOHuLjC2/r7a20PHNAeZ488CvvhyIPoybG2tsbatWthbW2N0NBQWFlZISQkxKQ+6tWrh02bNqFjx44ICQnB7t270bhx44J3Sk8Hjh4FYmO1b1d8EpKTtYtP81K7NnD1qlK3DA+ifDg4OOD27duIjo5Gly5d4OrqanIfvr6+WLduHYKDg9G9e3fs3bsXXn9OKfR6Pc789htu/vvf8Lx8GX/74w/g2DEgKyu7AO1bFOPitKlIgwYFv9ilS9rvatVMrlMFw4OoAD/88AMyMzOL9XWY3bp1w9KlS/Hqq6+iWbNm6NWrFxITE3Ho0CHcu3MHtwA4A4CHBxAUBPj7A35+WnBERQG7dgHbtgFdu+b/InFxwJUrQPXqOdc7SpLSZIeonOjZs6dYWFgofVvf47K/Lwd/XojXvn17iYyMlJ2TJsnlvXu1rxl5XFycdqi2UiWRgmp49dVSP1TLkQdRPu7evYvNmzejQ4cOcHd3L3Z/np6eAID58+dj8ODBsLIqwj+/Bg2At94CZs8GQkKA77/POS0xGIAPPwRWrgTq1gUiI4tdZ1ExPIjysWnTJjx48KBYU5ZsIoJ169bB09MTQ4cOhYWFRdF3/uQTbfF08WKgXj0gOFgLirQ0YMsW4OxZbfumTYCzc7FrLSoT3gFR+bJ69WoAQK9evYrd1/Hjx3H+/Hn06dPHtOAAACsr4KuvgK1bgRdfBPbs0dZCvv4aqFwZmDVLW2j19i52nSaVVaqvRlRGnDp1CuvXr4eXl5dxulEc3333HQCgb9++6p107Vrwounjdu4svE1iomo1HHkQ5cfR0RGXLl3Cl19+Wax+RARr1qyBh4cH2rZt+4SqMz+GB1EeGjVqhPj4eNSpUwfDhg3DDz/8oNzXqVOncObMGfTu3RuWlpZPsErzYngQ5aNq1arYvHkz3Nzc8PLLL2Pfvn1K/TyRKctTSCciYu4iiJ5mBw8eRMeOHWFnZ4eYmBj4+PiYtH/Tpk1x9epVJCcnF+3wbBnBkQdRIVq1aoXvvvsOqamp6N69O65cuVLkfY8cOYITJ06gZ8+ez1RwAAwPoiIJCgrCV199hQsXLiAoKAipeVy0ptfrcfLkSSxevBgRERFo3rw5fH19AWgB9Kx5tqKQqASFh4cjOTkZ48aNQ0hICJYtW4ajR48iNjYWBw4cwKFDh3Dnzh1j+2rVqiEgIABZWVkIDAw0Y+Ulg2seRCYQEYwYMQJffPFFju0ODg7w9fWFv78//Pz84O/vj+rVq0On05mp0pLH8CAy0aFDh9CqVSt4eHhg2rRp8Pf3R6NGjZ6pw7BFwWkLkYnWr18PAFizZg0CAgLMW4wZccGUyrbERECnA7p3z7/Nzp1am4iI3M+lpgJTpwKtWgEVKwJ2dkCdOkB4OPDrr7maiwi+++47eHh4oE2bNk/qXZRJDA8qvw4e1G4cPGEC8OABMGAAMHo00KwZsHq1djOeyZNz7HL62DGcOXMGvXr1KnfTlMdx2kLl08WL2mjl9m1g/vzco5IzZ7RL3ydNAtzdgREjAADX33kHZwCklPNRB8CRB5VX778P3LwJjBuX93SmQQNgwwbA2lpr8+d5HY3i4uBuaQn/l14q5YKfPgwPKn/u3QO+/VZb3yjozluNGwO9e2s33VmzBjh7FlWSk+EaHg4rO7vSq/cpxWkLPRvOndOmGHl5/J4Vhw4BmZnaTYYrViy43y5dgG++AfbtA/74Q9v2BO4s9ixgeNCz4fz5XIub+cr+npKi3GU8u01yMnD8uHabv8K+va2c4LSFng2Bgfl/J9qOHcXvPz1dG7H06AHY2ha/v2cAw4PKn+xvT8v+kqSCZLdxcNBuMswpixGnLVT++PpqR1EOH9aOori45N922zbtd2go8Prr2kiGAHDkQeWRgwPw0kvaiWGzZuXf7vRp7XtSnJyAvn21s1RNvfP5M4x/ElQ+TZ8OuLpqv/O6wfHZs0DPnsDDh8BHHxV+VKYc4rSFyqdatbQvSerZExgyBPj8c6BjR8DeXhtx/Pyzdjh30iTj2aWUE8ODyq+//137gui5c4GNG4GlS7WpTNWqQP/+wJtvAi1bmrvKpxbv50FESrjmQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpIThQURKGB5EpOT/ARPLUg7LSgD2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_and_save (smi = 'CC(=CC(C)(C)CCCCCCCC(=O)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C', fname='out.png')\n",
    "draw_and_save (smi = 'CC=C', fname='out.png', add_Hs=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5160b737-e75a-4ce5-b62a-e5e42e60f961",
   "metadata": {},
   "source": [
    "### Normalize properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e219a9c-0dce-44e5-872c-6be409c8575d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133885, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1)) \n",
    "y_data = scaler.fit_transform(y_data, )  #array-like of shape (n_samples, n_features)\n",
    "y_data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4fd7082-83cb-4dcc-99b5-d10a28fea63f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context_embedding_max_length=y_data.shape[1]\n",
    "context_embedding_max_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c05a756c-c23b-4431-8ef6-d8564c2bac8b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler(feature_range=(-1, 1))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler(feature_range=(-1, 1))</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler(feature_range=(-1, 1))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1b388f98-f035-4fb4-a6c5-36aff8f81a2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist (y_data[:,1])\n",
    "plt.show()\n",
    "plt.hist (y_data[:,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e8a13a4d-45c6-42ab-a636-863f9d03604f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_data_temp= df['smiles']\n",
    "start_char='@'\n",
    "end_char='$'\n",
    "\n",
    "X_data=[]\n",
    "for i in range (len (X_data_temp)):\n",
    "    X_data.append (start_char+X_data_temp[i]+end_char)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce110e5-f49c-4fa8-be69-998c5f8b950d",
   "metadata": {},
   "source": [
    "### Dataset loader "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd6476fe-9202-4f70-9622-20ed44b6e7e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "class RegressionDataset(Dataset):\n",
    "    \n",
    "    def __init__(self, X_data, y_data):\n",
    "        self.X_data = X_data\n",
    "        self.y_data = y_data\n",
    "       \n",
    "    def __getitem__(self, index):\n",
    "        \n",
    "        return self.X_data[index], self.y_data[index]\n",
    "        \n",
    "    def __len__ (self):\n",
    "        return len(self.X_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b9f0b698-2838-416f-8606-a2eed691d70e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def pad_sequence_end (output_xyz, max_length_l):         #pad\n",
    "    output=torch.zeros((output_xyz.shape[0] , max_length_l,  output_xyz.shape[2])).to(device)\n",
    "    output[:,:output_xyz.shape[-2],:]=output_xyz  \n",
    "    return output.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6f1bde27-459b-417c-b0a8-ab3f661451f1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_data_loaders (X_scaled,  y_data_scaled,  split=0.1, batch_size_=16):\n",
    "\n",
    "    X_train, X_test, y_train, y_test,     = train_test_split(torch.Tensor (X_scaled),    torch.Tensor (y_data_scaled) ,\n",
    "                                                             test_size=split,random_state=235)\n",
    "\n",
    "    print (f\"Shapes= {X_scaled.shape}, {y_data_scaled.shape}\")\n",
    "     \n",
    "    print(X_train.shape)\n",
    "    print(y_train.shape)\n",
    "    print(X_test.shape)\n",
    "    print(y_test.shape)\n",
    "    train_dataset = RegressionDataset(X_train, y_train,  ) #/ynormfac)\n",
    "\n",
    "    test_dataset = RegressionDataset(X_test,y_test, )\n",
    "    \n",
    "    train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size_, shuffle=True)\n",
    "    train_loader_noshuffle = DataLoader(dataset=train_dataset, batch_size=batch_size_, shuffle=False)\n",
    "    test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size_)\n",
    "\n",
    "    return train_loader,train_loader_noshuffle, test_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5afa0202-ae9c-4afb-8017-0b7b39695eb0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum length string is :  @c1(=O)[nH]c(=O)[nH]c(=O)[nH]1$ length= 31 chosen length= 64\n"
     ]
    }
   ],
   "source": [
    "res = max(X_data[:], key = len)\n",
    "max_length_data=len (res)\n",
    "max_length=max (max_length_data, 64)\n",
    "\n",
    "# printing result\n",
    "print(\"Maximum length string is : \" , res, \"length=\",max_length_data, \"chosen length=\", max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9bbd2d7e-161e-4f36-b8a1-a2780b8a52c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@C$', '@N$', '@O$', '@C#C$', '@C#N$', '@C=O$']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a47d9d5a-8755-4708-8a8e-7a07f79e69ea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import text, sequence\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63fcf622-7478-42b2-af2d-e7bab7a41cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "filters=''# '!\"$%&()*+,-./:;<=>?[\\\\]^_`{|}\\t\\n'\n",
    "\n",
    "tokenizer_X = Tokenizer(char_level=True, filters=filters, lower=False)\n",
    "tokenizer_X.fit_on_texts(X_data)\n",
    "        \n",
    "X_data_tokenized = tokenizer_X.texts_to_sequences(X_data)\n",
    "\n",
    "X_data_tokenized= sequence.pad_sequences(X_data_tokenized,  maxlen=max_length, padding='post', truncating='post') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fe8ebb45-4059-422d-b11e-a5036b0b5ac0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filters=''# '!\"$%&()*+,-./:;<=>?[\\\\]^_`{|}\\t\\n'\n",
    "\n",
    "tokenizer_X = Tokenizer(char_level=True, filters=filters, lower=False)\n",
    "tokenizer_X.fit_on_texts(X_data)\n",
    "        \n",
    "X_data_tokenized = tokenizer_X.texts_to_sequences(X_data)\n",
    "\n",
    "X_data_tokenized= sequence.pad_sequences(X_data_tokenized,  maxlen=max_length, padding='post', truncating='post')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fc592854-037f-41cf-b095-23533e63cfc2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[4]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generate start token\n",
    "\n",
    "start_char_token= tokenizer_X.texts_to_sequences( start_char )\n",
    "start_char_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b7e3d9d-de17-4789-b5f1-9d3cedddee75",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_char_token= tokenizer_X.texts_to_sequences( end_char )\n",
    "end_char_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c10434c3-f52f-4ea7-8704-45ecf4e935cb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def reverse_tokenize (tokenizer_X, X_data, X_norm_factor=1):\n",
    "    #X_data_tokenized_reversed[:12]\n",
    "    X_data_tokenized_reversed=tokenizer_X.sequences_to_texts ( (X_data*X_norm_factor).astype(int) )\n",
    "    k = []\n",
    "    ## k = lambda: [num.strip() for num in X_data_tokenized_reversed]\n",
    "    #k = lambda: [num.replace(' ', '') for num in X_data_tokenized_reversed]\n",
    "    for i in X_data_tokenized_reversed:\n",
    "        i=str(i).replace(' ','')\n",
    "        #print (i)\n",
    "        k.append(i)\n",
    "    return k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f79765dd-84b6-4acb-b663-c12e23fd0cc2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#################################\n",
      "DICTIONARY y_data\n",
      "{'num_words': None, 'filters': '', 'lower': False, 'split': ' ', 'char_level': True, 'oov_token': None, 'document_count': 133885, 'word_counts': '{\"@\": 133885, \"C\": 787482, \"$\": 133885, \"N\": 109306, \"O\": 181161, \"#\": 37027, \"=\": 106335, \"(\": 126532, \")\": 126532, \"1\": 270020, \"c\": 59074, \"[\": 9918, \"n\": 30458, \"H\": 8247, \"]\": 9918, \"o\": 6836, \"3\": 34756, \"+\": 1705, \"-\": 1808, \"2\": 121675, \"F\": 3314, \"4\": 5186, \"5\": 242}', 'word_docs': '{\"$\": 133885, \"@\": 133885, \"C\": 131656, \"N\": 74300, \"O\": 111215, \"#\": 32432, \"=\": 76005, \"(\": 92249, \")\": 92249, \"1\": 119887, \"H\": 7941, \"]\": 7941, \"n\": 16002, \"[\": 7941, \"c\": 17698, \"o\": 6546, \"+\": 1705, \"-\": 1808, \"3\": 17627, \"2\": 60295, \"F\": 2163, \"4\": 2593, \"5\": 121}', 'index_docs': '{\"5\": 133885, \"4\": 133885, \"1\": 131656, \"9\": 74300, \"3\": 111215, \"12\": 32432, \"10\": 76005, \"6\": 92249, \"7\": 92249, \"2\": 119887, \"17\": 7941, \"16\": 7941, \"14\": 16002, \"15\": 7941, \"11\": 17698, \"18\": 6546, \"22\": 1705, \"21\": 1808, \"13\": 17627, \"8\": 60295, \"20\": 2163, \"19\": 2593, \"23\": 121}', 'index_word': '{\"1\": \"C\", \"2\": \"1\", \"3\": \"O\", \"4\": \"@\", \"5\": \"$\", \"6\": \"(\", \"7\": \")\", \"8\": \"2\", \"9\": \"N\", \"10\": \"=\", \"11\": \"c\", \"12\": \"#\", \"13\": \"3\", \"14\": \"n\", \"15\": \"[\", \"16\": \"]\", \"17\": \"H\", \"18\": \"o\", \"19\": \"4\", \"20\": \"F\", \"21\": \"-\", \"22\": \"+\", \"23\": \"5\"}', 'word_index': '{\"C\": 1, \"1\": 2, \"O\": 3, \"@\": 4, \"$\": 5, \"(\": 6, \")\": 7, \"2\": 8, \"N\": 9, \"=\": 10, \"c\": 11, \"#\": 12, \"3\": 13, \"n\": 14, \"[\": 15, \"]\": 16, \"H\": 17, \"o\": 18, \"4\": 19, \"F\": 20, \"-\": 21, \"+\": 22, \"5\": 23}'}\n",
      "Length dictionary:  12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"#################################\")\n",
    "print (\"DICTIONARY y_data\")\n",
    "dictt=tokenizer_X.get_config()\n",
    "print (dictt)\n",
    "print (\"Length dictionary: \", len (dictt) )\n",
    "num_words = len(tokenizer_X.word_index) + 1\n",
    "num_words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be0dd7e2-b06c-4d4e-a995-54987dd0c8a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_data_tokenized_reversed=reverse_tokenize (tokenizer_X, X_data_tokenized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f1d63d77-c7c4-433f-a5be-25dcaf428473",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@C$', '@N$', '@O$', '@C#C$', '@C#N$', '@C=O$']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data [:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0b0b8a8f-8063-4da9-96ce-6d22d57bcf12",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@C$', '@N$', '@O$', '@C#C$', '@C#N$', '@C=O$']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_data_tokenized_reversed[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "308b9f81-0b2b-48d5-8d1a-204cf38a5d6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 1, 5, ..., 0, 0, 0],\n",
       "       [4, 9, 5, ..., 0, 0, 0],\n",
       "       [4, 3, 5, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [4, 1, 2, ..., 0, 0, 0],\n",
       "       [4, 1, 2, ..., 0, 0, 0],\n",
       "       [4, 1, 2, ..., 0, 0, 0]], dtype=int32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data_tokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "446a56e9-0ef0-4f84-86b3-33790ecaf5da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes= (133885, 64), (133885, 12)\n",
      "torch.Size([120496, 64])\n",
      "torch.Size([120496, 12])\n",
      "torch.Size([13389, 64])\n",
      "torch.Size([13389, 12])\n",
      "Number of batches in training: 471\n"
     ]
    }
   ],
   "source": [
    "batch_size_=256\n",
    "train_loader,train_loader_noshuffle, test_loader= get_data_loaders (X_data_tokenized,  y_data ,  split=0.1, \n",
    "                                                                    batch_size_=batch_size_)\n",
    "\n",
    "print (f\"Number of batches in training: {len (train_loader)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "133e2939-1723-4ea0-ab74-21960754a3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_handle =sns.histplot(np.array(X_data_tokenized).flatten(), bins=12,   log_scale=(False, True) )\n",
    "fig = fig_handle.get_figure()\n",
    "fig_handle.set_xlim(0, num_words)\n",
    "#fig_handle.set_ylim(0, 100000)\n",
    "#plt.xticks(range (1,21))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7e38e567-a306-442b-9d9e-8b198a61889f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ALL_SMILES=df['smiles'].values.tolist()\n",
    " \n",
    "def is_novel (ALL_SMILES, smi):\n",
    "    if smi in ALL_SMILES:\n",
    "        return False\n",
    "    else:\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "95f3cce6-023c-4988-ac91-9b03d3028d8a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133885, 64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data_tokenized.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3a41d7-afa1-4dfb-8122-35d54c5a5eed",
   "metadata": {},
   "source": [
    "### Set up transformer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "80ca3db2-6c34-4c48-92a5-f728e0c71330",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import MoleculeDiffusion \n",
    "from   MoleculeDiffusion import remove_start_end_token, MoleculeTransformer,MoleculeTransformerSequence, count_parameters,plot_results_as_barchart\n",
    "from   MoleculeDiffusion import  MoleculeTransformerSequenceEncoder, generate_from_conditioning_transformer,remove_start_end_token_first\n",
    "from   MoleculeDiffusion import    predict_properties_from_SMILES_transformer, train_loop_forward_transformer, sample_loop_forward_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df8a64a5-ebb0-4f3c-bc4b-d43e88d1f816",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prefix='./transformer_forward/'\n",
    "if not os.path.exists(prefix):\n",
    "        os.mkdir (prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f5293ea-0959-4cf5-bfb3-9918388243db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 64, 24)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context_embedding_max_length,max_length,num_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "843ed983-872c-4c3e-8ffd-2cd76dd1c0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_model=False #True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "49dc299d-0d27-4cb3-8eac-87f174c38e93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 12\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Total parameters:  3162496  trainable parameters:  3162496\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#max_length=64\n",
    "context_embedding_max_length=y_data.shape[1]\n",
    "logits_dim = 1 #\n",
    "print (logits_dim, context_embedding_max_length)\n",
    "\n",
    "model_forward = MoleculeTransformerSequenceEncoder(\n",
    "        dim=256,\n",
    "        depth=6,\n",
    "        logits_dim=logits_dim, #logits channels\n",
    "        logits_dim_length = context_embedding_max_length , #if None entire length is used. Otherwise, max_length is projected to logits_dim_length\n",
    "                                #  OUTPUT: (b, logits_dim, logits_dim_length)\n",
    "        max_length = max_length, #  \n",
    "        dim_head = 16,\n",
    "        heads = 16,\n",
    "        dropout = 0.1,\n",
    "        ff_mult = 2.,\n",
    "         max_tokens= num_words,\n",
    "        embed_dim = 16, #for sequence embedding\n",
    "       padding_token=0, #used for mask generation\n",
    "        \n",
    "    #max_text_len = context_embedding_max_length, #max length of conditioning sequence\n",
    "       # pos_fourier_graph_dim= 32, #entire graph fourier embedding, will be added to logits_dim\n",
    "              \n",
    ").to (device)\n",
    "\n",
    "\n",
    "count_parameters (model_forward)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "13c71455-a2c8-411b-8ee2-8b0d9a6b6ba1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 64])\n"
     ]
    }
   ],
   "source": [
    "seq_input=torch.randint (0,num_words, (4, max_length)).cuda()  #batch, 1, length (length is flexible)\n",
    "print (seq_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "97736614-5f0f-4d18-b52d-b770cc2a587b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 1, 12])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=model_forward(seq_input)\n",
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5995265-e89b-4936-ab6d-0aa4efb46b32",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "if train_model:\n",
    "\n",
    "    optimizer = optim.Adam(model_forward.parameters() , lr=0.0001 )\n",
    "    \n",
    "    loss_list=[]\n",
    "    R2_list=[]\n",
    "\n",
    "    train_loop_forward_transformer (model_forward,device,\n",
    "                    train_loader,test_loader,\n",
    "                    optimizer=optimizer, \n",
    "                    prefix=prefix,\n",
    "                    tokenizer_X=tokenizer_X,\n",
    "                    print_every=1000,\n",
    "                    epochs= 3000000,\n",
    "                    start_ep=0,\n",
    "                    start_step=0,\n",
    "                    print_loss=1*len (train_loader),\n",
    "                    num_samples=256,\n",
    "                    save_model=True,\n",
    "                    show_jointplot=True,\n",
    "                    loss_list=loss_list,\n",
    "                    R2_list=R2_list,                \n",
    "                    )\n",
    "    \n",
    "else:\n",
    "    # Load best model\n",
    "    fname=f'./QM_forward_transformer_2/statedict_save-model-epoch_10.pt' \n",
    "    model_forward.load_state_dict(torch.load(fname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2224f93f-07d5-40b6-b3ce-bb1e6319ef15",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res, _=predict_properties_from_SMILES_transformer (model_forward,device, SMILES=['C',\n",
    "                                                                     'CCC',\n",
    "                                                                     'CCCCCC',\n",
    "                                                                     'CCCCCCCCC'], \n",
    "                scaler=scaler,\n",
    "                tokenizer_X=tokenizer_X,\n",
    "                flag=0, \n",
    "                draw_molecules=False,\n",
    "                draw_all=False,\n",
    "                mols_per_row=8,max_length = 64,\n",
    "                context_embedding_max_length=12, \n",
    "               )\n",
    "\n",
    "plt.plot (res[:,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4977c46f-fef9-429a-ab95-0bf78b99d4a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sample_loop (model_forward,device,\n",
    "                test_loader,model_forward=model_forward,\n",
    "                num_samples=64, #how many samples produced every time tested.....\n",
    "                tokens_to_generate=max_length-1,show_jointplot=True ,\n",
    "                draw_molecules=True,\n",
    "                temperature=1.,\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55d7db8b-9faa-4159-ab78-f1682dc45826",
   "metadata": {},
   "outputs": [],
   "source": [
    "res, _=predict_properties_from_SMILES_transformer (model_forward,device, SMILES=['C',\n",
    "                                                                     'CCC',\n",
    "                                                                     'CCCCCC',\n",
    "                                                                     'CCCCCCCCC'], \n",
    "               # X_norm_factor=X_norm_factor,\n",
    "                scaler=scaler,\n",
    "                tokenizer_X=tokenizer_X,\n",
    "               \n",
    "             #   cond_scales=[1.], #list of cond scales - each sampled...\n",
    "                \n",
    "               # timesteps=100,\n",
    "                flag=0, \n",
    "              #  clamp=False,\n",
    "                 \n",
    "                draw_molecules=False,\n",
    "                draw_all=False,\n",
    "                mols_per_row=8,max_length = 64,\n",
    "                                       \n",
    "                context_embedding_max_length=12, \n",
    "               )\n",
    "\n",
    "plt.plot (res[:,:])\n",
    "plt.show()"
   ]
  }
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