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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"RDKit WARNING: [15:12:51] Enabling RDKit 2019.09.2 jupyter extensions\n"
]
}
],
"source": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from rdkit import Chem\n",
"from rdkit import RDPaths\n",
"\n",
"import dgl\n",
"import numpy as np\n",
"import random\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import DataLoader\n",
"from torch.utils.data import Dataset\n",
"from dgl import model_zoo\n",
"\n",
"from dgl.data.chem.utils import mol_to_complete_graph, mol_to_bigraph\n",
"\n",
"from dgl.data.chem.utils import atom_type_one_hot\n",
"from dgl.data.chem.utils import atom_degree_one_hot\n",
"from dgl.data.chem.utils import atom_formal_charge\n",
"from dgl.data.chem.utils import atom_num_radical_electrons\n",
"from dgl.data.chem.utils import atom_hybridization_one_hot\n",
"from dgl.data.chem.utils import atom_total_num_H_one_hot\n",
"from dgl.data.chem.utils import one_hot_encoding\n",
"from dgl.data.chem import CanonicalAtomFeaturizer\n",
"from dgl.data.chem import CanonicalBondFeaturizer\n",
"from dgl.data.chem import ConcatFeaturizer\n",
"from dgl.data.chem import BaseAtomFeaturizer\n",
"from dgl.data.chem import BaseBondFeaturizer\n",
"\n",
"from dgl.data.chem import one_hot_encoding\n",
"from dgl.data.utils import split_dataset\n",
"\n",
"from functools import partial\n",
"from sklearn.metrics import roc_auc_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def chirality(atom):\n",
" try:\n",
" return one_hot_encoding(atom.GetProp('_CIPCode'), ['R', 'S']) + \\\n",
" [atom.HasProp('_ChiralityPossible')]\n",
" except:\n",
" return [False, False] + [atom.HasProp('_ChiralityPossible')]\n",
" \n",
"def collate_molgraphs(data):\n",
" \"\"\"Batching a list of datapoints for dataloader.\n",
" Parameters\n",
" ----------\n",
" data : list of 3-tuples or 4-tuples.\n",
" Each tuple is for a single datapoint, consisting of\n",
" a SMILES, a DGLGraph, all-task labels and optionally\n",
" a binary mask indicating the existence of labels.\n",
" Returns\n",
" -------\n",
" smiles : list\n",
" List of smiles\n",
" bg : BatchedDGLGraph\n",
" Batched DGLGraphs\n",
" labels : Tensor of dtype float32 and shape (B, T)\n",
" Batched datapoint labels. B is len(data) and\n",
" T is the number of total tasks.\n",
" masks : Tensor of dtype float32 and shape (B, T)\n",
" Batched datapoint binary mask, indicating the\n",
" existence of labels. If binary masks are not\n",
" provided, return a tensor with ones.\n",
" \"\"\"\n",
" assert len(data[0]) in [3, 4], \\\n",
" 'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))\n",
" if len(data[0]) == 3:\n",
" smiles, graphs, labels = map(list, zip(*data))\n",
" masks = None\n",
" else:\n",
" smiles, graphs, labels, masks = map(list, zip(*data))\n",
"\n",
" bg = dgl.batch(graphs)\n",
" bg.set_n_initializer(dgl.init.zero_initializer)\n",
" bg.set_e_initializer(dgl.init.zero_initializer)\n",
" labels = torch.stack(labels, dim=0)\n",
" \n",
" if masks is None:\n",
" masks = torch.ones(labels.shape)\n",
" else:\n",
" masks = torch.stack(masks, dim=0)\n",
" return smiles, bg, labels, masks\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"atom_featurizer = BaseAtomFeaturizer(\n",
" {'hv': ConcatFeaturizer([\n",
" partial(atom_type_one_hot, allowable_set=[\n",
" 'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],\n",
" encode_unknown=True),\n",
" partial(atom_degree_one_hot, allowable_set=list(range(6))),\n",
" atom_formal_charge, atom_num_radical_electrons,\n",
" partial(atom_hybridization_one_hot, encode_unknown=True),\n",
" lambda atom: [0], # A placeholder for aromatic information,\n",
" atom_total_num_H_one_hot, chirality\n",
" ],\n",
" )})\n",
"bond_featurizer = BaseBondFeaturizer({\n",
" 'he': lambda bond: [0 for _ in range(10)]\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"train=os.path.join(RDPaths.RDDocsDir, 'Book/data/solubility.train.sdf')\n",
"test=os.path.join(RDPaths.RDDocsDir, 'Book/data/solubility.test.sdf')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"train_mols = Chem.SDMolSupplier(train)\n",
"train_smi =[Chem.MolToSmiles(m) for m in train_mols]\n",
"train_sol = torch.tensor([float(mol.GetProp('SOL')) for mol in train_mols]).reshape(-1,1)\n",
"\n",
"test_mols = Chem.SDMolSupplier(test)\n",
"test_smi = [Chem.MolToSmiles(m) for m in test_mols]\n",
"test_sol = torch.tensor([float(mol.GetProp('SOL')) for mol in test_mols]).reshape(-1,1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"train_graph =[mol_to_bigraph(mol,\n",
" atom_featurizer=atom_featurizer, \n",
" bond_featurizer=bond_featurizer) for mol in train_mols]\n",
"\n",
"test_graph =[mol_to_bigraph(mol,\n",
" atom_featurizer=atom_featurizer, \n",
" bond_featurizer=bond_featurizer) for mol in test_mols]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def run_a_train_epoch(n_epochs, epoch, model, data_loader,loss_criterion, optimizer):\n",
" model.train()\n",
" total_loss = 0\n",
" losses = []\n",
" \n",
" for batch_id, batch_data in enumerate(data_loader):\n",
" batch_data\n",
" smiles, bg, labels, masks = batch_data\n",
" if torch.cuda.is_available():\n",
" bg.to(torch.device('cuda:0'))\n",
" labels = labels.to('cuda:0')\n",
" masks = masks.to('cuda:0')\n",
" \n",
" prediction = model(bg, bg.ndata['hv'], bg.edata['he'])\n",
" loss = (loss_criterion(prediction, labels)*(masks != 0).float()).mean()\n",
" #loss = loss_criterion(prediction, labels)\n",
" #print(loss.shape)\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" losses.append(loss.data.item())\n",
" \n",
" #total_score = np.mean(train_meter.compute_metric('rmse'))\n",
" total_score = np.mean(losses)\n",
" print('epoch {:d}/{:d}, training {:.4f}'.format( epoch + 1, n_epochs, total_score))\n",
" return total_score"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"model = model_zoo.chem.AttentiveFP(node_feat_size=39,\n",
" edge_feat_size=10,\n",
" num_layers=2,\n",
" num_timesteps=2,\n",
" graph_feat_size=200,\n",
" output_size=1,\n",
" dropout=0.2)\n",
"model = model.to('cuda:0')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train_loader = DataLoader(dataset=list(zip(train_smi, train_graph, train_sol)), batch_size=128, collate_fn=collate_molgraphs)\n",
"test_loader = DataLoader(dataset=list(zip(test_smi, test_graph, test_sol)), batch_size=128, collate_fn=collate_molgraphs)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 1/100, training 10.3861\n",
"epoch 2/100, training 4.5270\n",
"epoch 3/100, training 3.9297\n",
"epoch 4/100, training 3.2830\n",
"epoch 5/100, training 3.1945\n",
"epoch 6/100, training 2.5470\n",
"epoch 7/100, training 2.4009\n",
"epoch 8/100, training 1.8935\n",
"epoch 9/100, training 1.1095\n",
"epoch 10/100, training 0.8527\n",
"epoch 11/100, training 1.2657\n",
"epoch 12/100, training 1.7431\n",
"epoch 13/100, training 1.1584\n",
"epoch 14/100, training 1.0794\n",
"epoch 15/100, training 0.9055\n",
"epoch 16/100, training 0.6647\n",
"epoch 17/100, training 0.6319\n",
"epoch 18/100, training 0.5721\n",
"epoch 19/100, training 0.5546\n",
"epoch 20/100, training 0.5408\n",
"epoch 21/100, training 0.5167\n",
"epoch 22/100, training 0.5643\n",
"epoch 23/100, training 0.5769\n",
"epoch 24/100, training 0.4929\n",
"epoch 25/100, training 0.4407\n",
"epoch 26/100, training 0.4709\n",
"epoch 27/100, training 0.5146\n",
"epoch 28/100, training 0.6413\n",
"epoch 29/100, training 0.7296\n",
"epoch 30/100, training 0.5777\n",
"epoch 31/100, training 0.6066\n",
"epoch 32/100, training 0.6477\n",
"epoch 33/100, training 0.4670\n",
"epoch 34/100, training 0.4940\n",
"epoch 35/100, training 0.6465\n",
"epoch 36/100, training 0.6806\n",
"epoch 37/100, training 0.4990\n",
"epoch 38/100, training 0.5293\n",
"epoch 39/100, training 0.5494\n",
"epoch 40/100, training 0.4764\n",
"epoch 41/100, training 0.5203\n",
"epoch 42/100, training 0.4671\n",
"epoch 43/100, training 0.4309\n",
"epoch 44/100, training 0.3848\n",
"epoch 45/100, training 0.4124\n",
"epoch 46/100, training 0.5451\n",
"epoch 47/100, training 0.5151\n",
"epoch 48/100, training 0.5509\n",
"epoch 49/100, training 0.4538\n",
"epoch 50/100, training 0.4530\n",
"epoch 51/100, training 0.4690\n",
"epoch 52/100, training 0.3676\n",
"epoch 53/100, training 0.3614\n",
"epoch 54/100, training 0.3786\n",
"epoch 55/100, training 0.3328\n",
"epoch 56/100, training 0.4372\n",
"epoch 57/100, training 0.4739\n",
"epoch 58/100, training 0.5069\n",
"epoch 59/100, training 0.5399\n",
"epoch 60/100, training 0.4183\n",
"epoch 61/100, training 0.3897\n",
"epoch 62/100, training 0.3679\n",
"epoch 63/100, training 0.3346\n",
"epoch 64/100, training 0.3378\n",
"epoch 65/100, training 0.3726\n",
"epoch 66/100, training 0.3599\n",
"epoch 67/100, training 0.3976\n",
"epoch 68/100, training 0.4378\n",
"epoch 69/100, training 0.3265\n",
"epoch 70/100, training 0.3570\n",
"epoch 71/100, training 0.3710\n",
"epoch 72/100, training 0.3459\n",
"epoch 73/100, training 0.4022\n",
"epoch 74/100, training 0.3992\n",
"epoch 75/100, training 0.3130\n",
"epoch 76/100, training 0.3087\n",
"epoch 77/100, training 0.3131\n",
"epoch 78/100, training 0.2712\n",
"epoch 79/100, training 0.2718\n",
"epoch 80/100, training 0.2672\n",
"epoch 81/100, training 0.2729\n",
"epoch 82/100, training 0.2522\n",
"epoch 83/100, training 0.2545\n",
"epoch 84/100, training 0.2500\n",
"epoch 85/100, training 0.2497\n",
"epoch 86/100, training 0.2639\n",
"epoch 87/100, training 0.2603\n",
"epoch 88/100, training 0.2790\n",
"epoch 89/100, training 0.2595\n",
"epoch 90/100, training 0.2842\n",
"epoch 91/100, training 0.3530\n",
"epoch 92/100, training 0.2823\n",
"epoch 93/100, training 0.2780\n",
"epoch 94/100, training 0.2480\n",
"epoch 95/100, training 0.2621\n",
"epoch 96/100, training 0.2996\n",
"epoch 97/100, training 0.2732\n",
"epoch 98/100, training 0.3606\n",
"epoch 99/100, training 0.4227\n",
"epoch 100/100, training 0.3557\n"
]
}
],
"source": [
"loss_fn = nn.MSELoss(reduction='none')\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=10 ** (-2.5), weight_decay=10 ** (-5.0),)\n",
"n_epochs = 100\n",
"epochs = []\n",
"scores = []\n",
"for e in range(n_epochs):\n",
" score = run_a_train_epoch(n_epochs, e, model, train_loader, loss_fn, optimizer)\n",
" epochs.append(e)\n",
" scores.append(score)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f0dde8732e8>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(epochs, scores)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AttentiveFP(\n",
" (init_context): GetContext(\n",
" (project_node): Sequential(\n",
" (0): Linear(in_features=39, out_features=200, bias=True)\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" )\n",
" (project_edge1): Sequential(\n",
" (0): Linear(in_features=49, out_features=200, bias=True)\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" )\n",
" (project_edge2): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=400, out_features=1, bias=True)\n",
" (2): LeakyReLU(negative_slope=0.01)\n",
" )\n",
" (attentive_gru): AttentiveGRU1(\n",
" (edge_transform): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=200, out_features=200, bias=True)\n",
" )\n",
" (gru): GRUCell(200, 200)\n",
" )\n",
" )\n",
" (gnn_layers): ModuleList(\n",
" (0): GNNLayer(\n",
" (project_edge): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=400, out_features=1, bias=True)\n",
" (2): LeakyReLU(negative_slope=0.01)\n",
" )\n",
" (attentive_gru): AttentiveGRU2(\n",
" (project_node): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=200, out_features=200, bias=True)\n",
" )\n",
" (gru): GRUCell(200, 200)\n",
" )\n",
" )\n",
" )\n",
" (readouts): ModuleList(\n",
" (0): GlobalPool(\n",
" (compute_logits): Sequential(\n",
" (0): Linear(in_features=400, out_features=1, bias=True)\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" )\n",
" (project_nodes): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=200, out_features=200, bias=True)\n",
" )\n",
" (gru): GRUCell(200, 200)\n",
" )\n",
" (1): GlobalPool(\n",
" (compute_logits): Sequential(\n",
" (0): Linear(in_features=400, out_features=1, bias=True)\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" )\n",
" (project_nodes): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=200, out_features=200, bias=True)\n",
" )\n",
" (gru): GRUCell(200, 200)\n",
" )\n",
" )\n",
" (predict): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=200, out_features=1, bias=True)\n",
" )\n",
")"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"all_pred = []\n",
"for test_data in test_loader:\n",
" smi_lst, bg, labels, masks = test_data\n",
" if torch.cuda.is_available():\n",
" bg.to(torch.device('cuda:0'))\n",
" labels = labels.to('cuda:0')\n",
" masks = masks.to('cuda:0')\n",
" pred = model(bg, bg.ndata['hv'], bg.edata['he'])\n",
" all_pred.append(pred.data.cpu().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"res = np.vstack(all_pred)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(257, 1)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(257, 1)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_sol.numpy().shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'exp')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.clf()\n",
"plt.scatter(res, test_sol)\n",
"plt.xlabel('pred')\n",
"plt.ylabel('exp')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8605614290390137\n"
]
}
],
"source": [
"print(r2_score(test_sol, res))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem\n",
"from rdkit.Chem import DataStructs"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"train_fp = [AllChem.GetMorganFingerprintAsBitVect(mol,2) for mol in train_mols]\n",
"test_fp = [AllChem.GetMorganFingerprintAsBitVect(mol,2) for mol in test_mols]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"rfr = RandomForestRegressor()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/takayuki/anaconda3/envs/chemo37/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
" \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
"/home/takayuki/anaconda3/envs/chemo37/lib/python3.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
},
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10,\n",
" n_jobs=None, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rfr.fit(train_fp, train_sol)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"rfr_pred = rfr.predict(test_fp)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6881015769353733"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2_score(test_sol, rfr_pred)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f0ddc2e17f0>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bEAEOT87i8RcvQhVdJ+WM7RvBzqMvZTTqTls2NrB//HSsGnjThadMv5c6YCCn0nI50CFtjXgwv373bdvx6utzifLttlNw4ixE+g9SCOvL4loBE0djQHBjaWX9eeMeTuGXRe1+WdY+qoCBnArh8kfqUpKW5vQe02LqM2cvr3/f5eSZ4Pv4lVu24uwPr63nlOME8YZIaBrDv6h45OAuHPnm+VSbekTe3Si0bagJ1e7DHFwPpwhKe6qS60I3rUrVNCspNs2qt6ybWCWZuU3NtPDVZ887HWgwPNjElk0bup4/6YEL24aa+Nn1JbR9TUyiKkk8/iZZpo6KSblUssSdGaeZUds2N8VtzNVvbE2zOCOnxJL+oUadSBNX3Ppj10oMz/xie32m6p8ZJj1wYWjjBtzzkX/SlcIJ6zni8acmxvaNYPqNN/H1s5dT15EvtpetBzbYgmdWNf4mzLHHw0BOiUR99A37Iy/6jzRpAPYstpfx+IsXMZ9wJtyaX8TJcy3jJ5CwGX4wNTE108okiHuWVbtm5rZ0SK9TH3n2x+kHDOSUSFTZX9gfedF/pFlcMK4ttLEtRRdB0yeQ4JrA8FreOli14pk4dSnTHZ3eyTymC3Dwwvz2O0uZfqoKSptjrxsGckokbFYdlTop+o80qx2Squ65bRPTz9C0wHv83t2JThAyCRvvsqoxHWKafdtk9amKvVfi4YYgSsQ2e/7A2qKYiXe7f2OLYDUHm+duv6x2SM4vtjveR1wDIl07UOPsVA37BLNlY/f7EwD33z5i3VAka68fFCcVleWnqrF9Izhz9AB+NH4Pzhw9wCAegoGcEglrOxoW5D1F/pFmtUNyQDrfh+35hgebxguHqRVsnJ2qRw7uQtPQjnZAVtsCBCmAV1+fw5GDu4wXHl17/SDXWTZTH8VhIKdEwmbVVegt7QXg//TA3q6xDmA1GEZZ0c4ZrO19H793N568bw8ahtPqg0E6zkLw2L4RvGdzd3Z0Re3b8L20iC23bmvWZbJtqFnYpyrqxBw5JWYrLytLfjPJpqOtg028fWPJeaONf3Ev6n3bWsH6c85xF4KTLLY+8vwF60KtrVmXaU3j2CFz7p7yx0BOPVF0b+k45XH+se4fP23sJ25jOiDB9r5tQdrLTSdZCLbVfguAzZaFzcX2Mq4bbg9r1gUUf2EmOwZy6kuum47SHJQMxFvc8w5oCIZdLzcdPJA4LGh647ZtalIAT963Bw9bPgUEH7VtqBk6wy76wkzhGMipL7nkmuMclLxtqInr7ZVUJZNj+0asgdV24HKQ67Z8Lxvv2lxraCPby1YZAzmVUtLt/97jbBlu/ww67KDkYJOoY4d2rz/GPyYAHW1fo8ZpC6wuM/s4vV28Wb4pVWPCre/VxkBOpZN0+3dUoAvOoKMOSjYF52BaJjjOhydncfyFi9ZNPGk2Q8VtLXB1frErVWM7so1b36uNgZxKJ2lTrbBAN2KYLdty4q4d9myvN7/YDl1Y9R7r34Z/eHJ2fQYd90R6Gy84+1M1ts6TZSoNpfgYyKkn0rQwTdpUy/Z9AYyB2TY79g5Kjhp72HjCLjxeYI37ySPOYiwrUOqFgZwyl7YzXtKmWnEfZwpqd9+2HSfPtZzGHhVYoy48x1+4GOuTh63qJcj06cMvSQUKT+spNwZyylzafuNReWRbUEmSfw4Gtf3jp0PH7n/trYNNNBti3TwUduGZmmlZ69VtF4Co/uNpDucIw9N6yo+BnDKXtt942Md/l6CSZuYYNvbga88vttEcEGzZ2MDbNzqDf9QFxNTTxLN1sGlN7TwxtgejH7pp/RAKb0NQ1Cw8jawPAqHsMZBT5rI+ENkfoKKCii1tEJxJiwDzC919vsPGbnrt9orifUMb8e//1a6O4OrvoRI3v/72jSXjiUT+ypk8A2jRB4FQNDbNosylaZoV1cY1SVAJPuf8YhvXFtrG5w8be9hr+5uFeeV9weeemmlh//hp3Hr0JQwYGmgBq826gqkaW/fDPEzNtKxjZclieTCQU+bS9BuPauMa1iLXHyj3j59eD6BR9dfB2bNt7FHtecPGHryYmGq5B5sNrFhWMouY/YadbcqSxXJhaoV6IunH/6gZd1jJoC137hIEw7bIexcI0xZ+f0CLe2oSsNr0akV1vWLmG69dKc2GnbAxs2VtuXBGTqUSNeu1zZhffX3OOht2CYK2+/hn0sC7W/iB7k8aSU5NWlHFj8bvwZGDu3DyXKtUs9+wMTOIlwsDOZWKS37ddLpQ2Gw46mg3WXtdE1s/Fm/3pz+gpTk1Kc/Zry0FZRub6+1UHAZyKpWk+fWwoOM9p43CXg8d98SesFOTgseyNQckMi2T9ew3zpmgVTjpiVZlkiMXkd8FMAFgu6r+NIvnpP5nKzNMkl+P2gw0tm9kvTwwKOzsziS7Ra1jDxZ/+L5OW7LpKk5NOLfzV0fqQC4iOwB8CsDl9MOhush6t6Btu/3EqUs4PDlr3H4PRM8wbW1gF24srZ/q42Li1KWussL2sq4H0DRdEeOIW77JAyWqIYvUylMAvgb7ea5EXeKcFu/Knzv3Fg/9KYST51q4//YRDA821x+zuWn/E/A+MSy2l7sm09cW2jjyzfPW/HJQVABNU7IZB/Pe/SnVjFxE7gXQUtXzYtk04LvvQwAeAoBbbrklzctSH7A1m4ra2OP6Md92ofif5/8v3llaWb/t2oK55WzwE4NpltJeVjz+4sXMGoHFmf0mbWKV18yf8hUZyEXkFQDvN3zrUQC/D+DTLi+kqicAnACA0dFRzt5rbGqmZT1SLaoM0DUVY7sgmBpVmXLEroc4XFtoOwXVLANomrQU8979SdRyeGvkA0X2APgbAAtrN30QwFUAd6jqP4Q9dnR0VKenpxO9LhUri3am3uaaIAHw1AN7jc9ne4ztEAjb/cP4TwWK89jBwGn1ti6EWbWCjfuzSIJta8tJRM6p6mjw9sSpFVW9AOB9vhf4MYBRVq30r6wWKMOOWMuiDBCwz4A3NweMBxcL3k33hB3C3PU4QawqkCyCYa+bWLFtbfWwjpycpVmgdGkYFVUGGOf2sX0juP/2ETTWXqshgvtvH8GxQ7uNm4OCQdu/g9OmOSCwfaDtZW+UXi9Y9mIhmnors0Cuqjs5G+9vUb26bbsFXRpGAcDOn7MHItvmlLtv2459f/gydh59CTuPvoS9j7+MqZkWpmZaHVvel1Vx8tzqmJ68b09H5YqNt4PTqyL5/F23dHw98esftV58elUFMjXTwsKNpa7bs1ywZNva6mHTLHJmyx1vHWyGfhR3XTg884M38djUBTwx1r0L01YnPvmdKx312fOLbRx57jzes3mDdVZ55ugBTJy6ZD2hx7NtqOmUc86rCsR0cDIADA82cfze3ZmlPfLanETZYWqFnNlmxWF5YiDeTO4br12xfi/YY+XV1+eMx6y1V9SYB/ePxWVMP7u+FFknnlf9N2CvpNmyaUOmr8et+dXDGTk5s5WuHZ6cNd7fC5ZxqkBsaZew54/Dm1W6jKm9osZFy6C8dj/mlfJgiWL1MJBTLME/8olTlzA81DTOgL2gadvmbtKI2Fjmt3WwaU2PDA828c7SijXl4TqmMuWF46Q80pYPcmt+tTC1QrGYuuf97PoSmo3OABxsWBVMP+z/hZuMz//gnTucxxIW84/fuzs05REck+0CUqa8sGvKI06HQ+oPnJFTLLYDiIcHm9iyaYN1Bmia4T02dWH9RJyGCB68c4dxodNm3pIH917P/7+2+3jfNy0kli0v7Jry4Kn39cNATrHYUg1vLbYxe8ypW8O6J8b2xArcQbZUQ1g9uk1V8sIuKQ+WD9YPAznFUqbStKwbQJUpL5wmx12m3xHlgzlyiqVMpWl5lv7lyTXHbduEVabfEeWDM3KKpWwpiDLNorPikuN26YdSlt9RUerU+IuBnGLrx+BZJi457qhgX/ffUd0afzGQU+WlrX4pG5ccNxc0w9Wtcoc5cqq0x6Yu4JmzlzuaYz1z9jIem7oQ+diwRl9Fcslx88i2cHW70DGQU6XZerOE9WwByr1pxmURlwua4ep2oWNqhZxltXiU5SKUrTdLVM+Wsn/0jspxc0EzXN3OJmUgpy6mQAsgk8WjrBehGiLGoB3Vs6UfPnrXfUEzTN0udAzk1MEWaDdtGIg1g7XNurOeCT945w48c/ay8fYw3DTT/+p0oWOOnDrYAq2ty6BpBhuWf856JvzE2B58/q5bOo50+/xdt0RWrTDHTP2EM3LqEDegmmawYbPuXsyEk/RsCX70Hh5qQhU4PDmLiVOX+vpjOPUfzsipQ5yA2hwQ4ww2bNad1Uw4i9JB78Shpx7Yi+vtFcwvtktXwULkgjNy6mBa7Rd0nzIPAO/ZbD5iLGzWHXcRqpcLr56yV7AQRWEgpw6mQGs7Es3WDzyq9Mt1ESqrhdco/VDBQvXG1Ap1MM2Abf29bWmYrLoSZrHw6qJum0eo/3BGXlNxUhb33z6Ck+dasTZXxJl129IsWSy8uqjb5hHqPwzkNWRLWWxumlMWr74+hyfv25P55oqozUG2w5WHmgNQSKYHSgD12TxC/YeBvIZsKQvbifJX5xd7srkiapHRtjlzU7OBY4d2Zxp467R5hPoPA3kN5ZWySDoO73bbYur8QpuBl8iHi501ZAvMw4PNXHc7Ri0ychGSyA0DeQ3ZNuUcv3d3rmdgRm0O4jZ6IjepUysi8jsAvgJgCcBLqvq11KOinopa3MsrZeE6Di5CEoUTjejbHPpgkbsBPArgHlV9R0Tep6r/GPW40dFRnZ6eTvy6VVOnQ2CJqHdE5JyqjgZvTzsj/zKAcVV9BwBcgnjd1O0QWCLKX9oc+YcB/JqIvCYifysiH7PdUUQeEpFpEZmem5tL+bLVEVZiR0SUhcgZuYi8AuD9hm89uvb4bQDuAvAxAM+KyM+rIV+jqicAnABWUytpBp1UESkO9vEgol6LDOSq+knb90TkywCeXwvcfyciKwBuBlC6KXdRKQ6eRENEvZY2Rz4F4ACAb4vIhwFsBPDT1KPqgTStStPM5JP08ShycZQLs0TVkzaQPw3gaRH5LoAbAL5gSquUQdIUR9qZfJL+20UtjvbLwiwvRlQ3qQK5qt4A8PmMxtJTSVMcWRw6EGc7eZGHHPTDAQv9cjEiiqM2OzuT7hLMe7GyyMXRfliYZZUQ1VFtAnnSww7y7vdRZH+Rfuht0g8XI6K4atX9MEnHvLwPHSjykIMyHrAQN9/NKiGqo9rMyJPK6tiysr5e8LXvv30EjbVG4A0R3H+7+8Uvi5Ptg8/3yPMX0JpfdD7dno22qI5S9VpJqm69VqoiuFAIrAZBlwtJmsfa7B8/bZxdjwwP4szRA6FjYdUK9aNe9VqhPpKmaqUXFS9J8908dILqhqkVWpdmobAXi4z9sPhKlAcG8gxlnSPOW5rA2Yugy3w3kRsG8owkWZgrmzSBsxdBt8iFX6IqYY48I/2wKzLNiTy9Os2H+W6iaAzkGemXjShpAieDLlExmFrJCBfmiKgoDOQZ4cIcERWFqZWMmHLEd9+2HROnLuHw5Cw3plQENxNRFTGQZ8ifI65zO9WqBsM6/86o2pha6ZG6tlOtchlmXX9nVH0M5D3SL1UscVU5GNb1d0bVx9RKj9S1naprMCxj+qWuvzOqPs7Ie6SuVSwuZZhlTb/U9XdG1cdA3iN13V7uEgzLmn6p6++Mqo+plR6q405Hl636Zc5F1/F3RtXHQE6ZiwqGzEUTZYupFcodc9FE2arMjLyMVQ6UTK86JRLVVSUCOXfc9R/moomyU4nUSlmrHIiIyqASgbzMVQ5EREWrRCBnr28iIrtK5MiPHNzVkSMHVqsc7r5tO/aPn+7JglmvF1e5eEtEWUkVyEVkL4D/AmAzgCUA/1ZV/y6LgfnZen2fPNfqyQJorxdXuXhLRFkSVU3+YJGXATylqn8lIv8CwNdU9Z9FPW50dFSnp6cTvy4A7B8/bdxUMjI8iDNHD/T0udPOpns5diLqXyJyTlVHg7enTa0ogPeu/XsrgKspn89ZLxdAw547i9l00Yu3TOsQ9Ze0i50PA5gQkSsA/hjAI+mH5KaXC6Bhz51FKWSRi7dl7TxIRMlFBnIReUVEvmv477AqDE8AAASmSURBVLMAvgzgsKruAHAYwJ+HPM9DIjItItNzc3OpB97Lbd5hz53FbLrILeqsySfqP5GpFVX9pO17IvLfAPy7tS+fA/BnIc9zAsAJYDVHHm+Y3Xq5zTvsuSdOXUrd8KnILepFp3WIKHtpc+RXAXwcwLcBHADw92kHFEcvt3nbnttWChl3Nl3UFnV2HiTqP2lz5F8E8B9F5DyA/wDgofRDKreqHz7AzoNE/SdV+WFSWZQfUnKsWiGqpl6VH1IFsfMgUX+pRK8VIiKyYyAnIqo4plaocpjjJ+rEQE6VwoZjRN2YWqFK4c5Uom4M5FQp3JlK1I2BnCqFp0URdWMgp0rhzlSiblzspEopsuEYUVkxkFPlcGcqUSemVoiIKo6BnIio4hjIiYgqjjlyH279JqIqYiBfw63fRFRVTK2s4dZvIqoqBvI13PpNRFXFQL6GW7+JqKoYyNdw6zcRVRUXO9dw6zcRVRUDuQ+3fhNRFTG1QkRUcQzkREQVx0BORFRxDORERBXHQE5EVHGiqvm/qMgcgDcCN98M4Ke5D6Y6+POJxp9RNP6MwpX95/MhVd0evLGQQG4iItOqOlr0OMqKP59o/BlF488oXFV/PkytEBFVHAM5EVHFlSmQnyh6ACXHn080/oyi8WcUrpI/n9LkyImIKJkyzciJiCgBBnIiooorVSAXkb0iclZEZkVkWkTuKHpMZSMivyMil0Tkooj8UdHjKSsR+V0RURG5ueixlImITIjI6yLyv0Xkv4vIcNFjKgsR+cza39b3ReRo0eOJo1SBHMAfAXhcVfcC+IO1r2mNiNwN4LMAPqKquwH8ccFDKiUR2QHgUwAuFz2WEvoWgF9W1Y8A+B6ARwoeTymISAPAfwbwzwH8EoAHReSXih2Vu7IFcgXw3rV/bwVwtcCxlNGXAYyr6jsAoKr/WPB4yuopAF/D6v+fyEdVX1bVpbUvzwL4YJHjKZE7AHxfVX+oqjcA/AVWJ02VULZA/jCACRG5gtXZJmcLnT4M4NdE5DUR+VsR+VjRAyobEbkXQEtVzxc9lgr4bQB/VfQgSmIEwBXf1z9Zu60Scj8hSEReAfB+w7ceBfAJAIdV9aSI/AaAPwfwyTzHV7SIn88GANsA3AXgYwCeFZGf15rVkEb8jH4fwKfzHVG5hP18VPV/rN3nUQBLAL6e59hKTAy3VebvqlR15CLyFoBhVVUREQBvqep7ox5XFyLy11hNrXx77esfALhLVecKHVhJiMgeAH8DYGHtpg9iNT13h6r+Q2EDKxkR+QKALwH4hKouRN2/DkTkVwEcV9WDa18/AgCq+mShA3NUttTKVQAfX/v3AQB/X+BYymgKqz8XiMiHAWxEuTu15UpVL6jq+1R1p6ruxOrH419hEH+XiHwGwO8BuJdBvMN3APyiiNwqIhsB/CaAFwoek7OyHb78RQB/IiIbAFwH8FDB4ymbpwE8LSLfBXADwBfqllah1P4UwCYA31r90IuzqvqlYodUPFVdEpGvADgFoAHgaVW9WPCwnJUqtUJERPGVLbVCREQxMZATEVUcAzkRUcUxkBMRVRwDORFRxTGQExFVHAM5EVHF/X8z3Q7JbD2ltQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.clf()\n",
"plt.scatter(rfr_pred, test_sol)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"from rdkit.Chem import rdDepictor\n",
"from rdkit.Chem.Draw import rdMolDraw2D\n",
"from IPython.display import SVG\n",
"from IPython.display import display\n",
"import matplotlib\n",
"import matplotlib.cm as cm\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def drawmol(idx, dataset, timestep):\n",
" smiles, graph, _ = dataset[idx]\n",
" print(smiles)\n",
" bg = dgl.batch([graph])\n",
" atom_feats, bond_feats = bg.ndata['hv'], bg.edata['he']\n",
" if torch.cuda.is_available():\n",
" print('use cuda')\n",
" bg.to(torch.device('cuda:0'))\n",
" atom_feats = atom_feats.to('cuda:0')\n",
" bond_feats = bond_feats.to('cuda:0')\n",
" \n",
" _, atom_weights = model(bg, atom_feats, bond_feats, get_node_weight=True)\n",
" assert timestep < len(atom_weights), 'Unexpected id for the readout round'\n",
" atom_weights = atom_weights[timestep]\n",
" min_value = torch.min(atom_weights)\n",
" max_value = torch.max(atom_weights)\n",
" atom_weights = (atom_weights - min_value) / (max_value - min_value)\n",
" \n",
" norm = matplotlib.colors.Normalize(vmin=0, vmax=1.28)\n",
" cmap = cm.get_cmap('bwr')\n",
" plt_colors = cm.ScalarMappable(norm=norm, cmap=cmap)\n",
" atom_colors = {i: plt_colors.to_rgba(atom_weights[i].data.item()) for i in range(bg.number_of_nodes())}\n",
"\n",
" mol = Chem.MolFromSmiles(smiles)\n",
" rdDepictor.Compute2DCoords(mol)\n",
" drawer = rdMolDraw2D.MolDraw2DSVG(280, 280)\n",
" drawer.SetFontSize(1)\n",
" op = drawer.drawOptions()\n",
" \n",
" mol = rdMolDraw2D.PrepareMolForDrawing(mol)\n",
" drawer.DrawMolecule(mol, highlightAtoms=range(bg.number_of_nodes()),\n",
" highlightBonds=[],\n",
" highlightAtomColors=atom_colors)\n",
" drawer.FinishDrawing()\n",
" svg = drawer.GetDrawingText()\n",
" svg = svg.replace('svg:', '')\n",
" if torch.cuda.is_available():\n",
" atom_weights = atom_weights.to('cpu')\n",
" return (Chem.MolFromSmiles(smiles), atom_weights.data.numpy(), svg)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"target = test_loader.dataset\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CCC(C)CC\n",
"use cuda\n"
]
}
],
"source": [
"res = drawmol(0, target, 0)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CCC(C)CC\n",
"use cuda\n"
]
},
{
"data": {
"image/svg+xml": [
"<svg baseProfile=\"full\" height=\"280px\" version=\"1.1\" viewBox=\"0 0 280 280\" width=\"280px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<!-- END OF HEADER -->\n",
"<rect height=\"280\" style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"280\" x=\"0\" y=\"0\"> </rect>\n",
"<ellipse cx=\"248.062\" cy=\"77.6105\" rx=\"19.211\" ry=\"19.211\" style=\"fill:#FF6E6E;fill-rule:evenodd;stroke:#FF6E6E;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<ellipse cx=\"212.041\" cy=\"140\" rx=\"19.211\" ry=\"19.211\" style=\"fill:#0000FF;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<ellipse cx=\"140\" cy=\"140\" rx=\"19.211\" ry=\"19.211\" style=\"fill:#FF6E6E;fill-rule:evenodd;stroke:#FF6E6E;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<ellipse cx=\"103.979\" cy=\"202.389\" rx=\"19.211\" ry=\"19.211\" style=\"fill:#1E1EFF;fill-rule:evenodd;stroke:#1E1EFF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<ellipse cx=\"103.979\" cy=\"77.6105\" rx=\"19.211\" ry=\"19.211\" style=\"fill:#1E1EFF;fill-rule:evenodd;stroke:#1E1EFF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<ellipse cx=\"31.9383\" cy=\"77.6105\" rx=\"19.211\" ry=\"19.211\" style=\"fill:#1E1EFF;fill-rule:evenodd;stroke:#1E1EFF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-0\" d=\"M 248.062,77.6105 L 212.041,140\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-1\" d=\"M 212.041,140 L 140,140\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-2\" d=\"M 140,140 L 103.979,202.389\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-3\" d=\"M 140,140 L 103.979,77.6105\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-4\" d=\"M 103.979,77.6105 L 31.9383,77.6105\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
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"CC(C)CC(C)C\n",
"use cuda\n"
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"use cuda\n"
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"C1=CCCCC1\n",
"use cuda\n"
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"CCc1ccccc1\n",
"use cuda\n"
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"Cc1cc(C)cc(C)c1\n",
"use cuda\n"
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"c1ccc2c(c1)CCC2\n",
"use cuda\n"
]
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"CC(C)Cc1ccccc1\n",
"use cuda\n"
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"CCCCCCc1ccccc1\n",
"use cuda\n"
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"c1ccc(CCc2ccccc2)cc1\n",
"use cuda\n"
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"CCc1cccc2ccccc12\n",
"use cuda\n"
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},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Cc1cc(C)c2ccccc2c1\n",
"use cuda\n"
]
},
{
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"name": "stdout",
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"text": [
"c1ccc2cc3ccccc3cc2c1\n",
"use cuda\n"
]
},
{
"data": {
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"<path class=\"bond-12\" d=\"M 101.525,162.214 L 63.0497,184.427\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-12\" d=\"M 91.3108,157.851 L 64.3782,173.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Cc1c2ccccc2c(C)c2ccccc12\n",
"use cuda\n"
]
},
{
"data": {
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"<path class=\"bond-1\" d=\"M 140,184.427 L 178.475,162.214\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-1\" d=\"M 141.329,173.4 L 168.261,157.851\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-16\" d=\"M 178.475,162.214 L 178.475,117.786\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-4\" d=\"M 255.425,162.214 L 255.425,117.786\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-6\" d=\"M 216.95,95.5727 L 178.475,117.786\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-8\" d=\"M 140,95.5727 L 140,51.1454\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-9\" d=\"M 140,95.5727 L 101.525,117.786\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-12\" d=\"M 33.46,124.45 L 33.46,155.55\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-13\" d=\"M 24.5745,162.214 L 63.0497,184.427\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-14\" d=\"M 64.3782,173.4 L 91.3108,157.851\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"</svg>"
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},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"c1ccc2cc3cc4ccccc4cc3cc2c1\n",
"use cuda\n"
]
},
{
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{
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"text": [
"c1ccc2c(c1)ccc1c3ccccc3ccc21\n",
"use cuda\n"
]
},
{
"data": {
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"<svg baseProfile=\"full\" height=\"280px\" version=\"1.1\" viewBox=\"0 0 280 280\" width=\"280px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<!-- END OF HEADER -->\n",
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"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"c1ccc2c(c1)ccc1nccnc12\n",
"use cuda\n"
]
},
{
"data": {
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"name": "stdout",
"output_type": "stream",
"text": [
"Nc1cc2c3ccccc3ccc2c2ccccc12\n",
"use cuda\n"
]
},
{
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"<path class=\"bond-17\" d=\"M 42.7801,147.39 L 78.5983,144.434\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-17\" d=\"M 47.5617,139.783 L 72.6344,137.714\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<text style=\"font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF\" x=\"58.9527\" y=\"219.651\"><tspan>NH</tspan><tspan style=\"baseline-shift:sub;font-size:17.25px;\">2</tspan><tspan/></text>\n",
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},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cc1ccc2cc3c(ccc4ccccc43)c3c2c1CC3\n",
"use cuda\n"
]
},
{
"data": {
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"<!-- END OF HEADER -->\n",
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