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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch_geometric.data import Data\n",
"from ogb.utils import smiles2graph\n",
"import os\n",
"import json\n",
"from rdkit import RDLogger\n",
"from rdkit import Chem\n",
"RDLogger.DisableLog('rdApp.*')\n",
"from tqdm import tqdm\n",
"import multiprocessing\n",
"\n",
"def write_json(data, filename):\n",
" with open(filename, 'w') as f:\n",
" json.dump(data, f, indent=4, ensure_ascii=False)\n",
"\n",
"def read_json(filename):\n",
" with open(filename, 'r') as f:\n",
" data = json.load(f)\n",
" return data\n",
"\n",
"def smiles2data(smiles):\n",
" graph = smiles2graph(smiles)\n",
" x = torch.from_numpy(graph['node_feat'])\n",
" edge_index = torch.from_numpy(graph['edge_index'], )\n",
" edge_attr = torch.from_numpy(graph['edge_feat'])\n",
" data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr)\n",
" return data\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# make pretrain graphs\n",
"root = 'data/pretrain_data/'\n",
"mol_property_list = read_json(f'{root}/Abstract_property.json')\n",
"target_file = f'{root}/mol_graph_map.pt'\n",
"\n",
"if not os.path.exists(target_file):\n",
" mol_graph_map = {}\n",
" for mol_dict in tqdm(mol_property_list):\n",
" smiles = mol_dict['canon_smiles']\n",
" graph = smiles2data(smiles)\n",
" mol_graph_map[smiles] = graph\n",
" torch.save(mol_graph_map, target_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# make downstrem (action prediction) graphs\n",
"root = 'data/action_data'\n",
"target_file = f'{root}/mol_graph_map.pt'\n",
"\n",
"if not os.path.exists(target_file):\n",
" all_mols = set()\n",
" reaction_list = read_json(f'{root}/processed.json')\n",
" rxn_keys = ['REACTANT', 'PRODUCT', 'CATALYST', 'SOLVENT']\n",
"\n",
" for rxn in reaction_list:\n",
" for key in rxn_keys:\n",
" for mol in rxn[key]:\n",
" if mol in all_mols:\n",
" continue\n",
" all_mols.add(mol)\n",
" mol_graph_map={}\n",
"\n",
" for smiles in all_mols:\n",
" graph = smiles2data(smiles)\n",
" mol_graph_map[smiles] = graph\n",
" torch.save(mol_graph_map, target_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# make downstream (retrosynthesis) graphs\n",
"root = 'data/synthesis_data'\n",
"\n",
"for folder in [\n",
" 'USPTO_50K_PtoR',\n",
" 'USPTO_50K_PtoR_aug20',\n",
" 'USPTO-MIT_PtoR_aug5',\n",
" 'USPTO-MIT_RtoP_aug5_mixed',\n",
" 'USPTO-MIT_RtoP_aug5_separated',\n",
" 'USPTO_full_pretrain_aug5_masked_token',\n",
" ]:\n",
" mol_graphid_file = f'{root}/{folder}/mol_graphid_map.json'\n",
" target_file = f'{root}/{folder}/idx_graph_map.pt'\n",
" if not os.path.exists(mol_graphid_file):\n",
" canon_idx_map = {}\n",
" mol_idx_map = {}\n",
" mol_set = set()\n",
" for mode in ['train', 'val', 'test']:\n",
" for file in ['src', 'tgt']:\n",
" if 'pretrain' in folder:\n",
" if file=='src':\n",
" continue\n",
" else:\n",
" if file=='tgt':\n",
" continue\n",
" file_path = f'{root}/{folder}/{mode}/{file}-{mode}.txt'\n",
" with open(file_path) as f:\n",
" lines = f.readlines()\n",
" for line in lines:\n",
" line = line.strip().replace(' ', '')\n",
" line = line.replace('<separated>', '.')\n",
" for smi in line.split('.'):\n",
" mol_set.add(smi)\n",
" smi_list = list(mol_set)\n",
" pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n",
" canon_list = pool.map(func=Chem.CanonSmiles,iterable=smi_list)\n",
" for smi, canon in zip(smi_list, canon_list):\n",
" if canon not in canon_idx_map:\n",
" canon_idx_map[canon] = len(canon_idx_map)\n",
" mol_idx_map[smi] = canon_idx_map[canon]\n",
" write_json(mol_idx_map, mol_graphid_file)\n",
" else:\n",
" mol_idx_map = read_json(mol_graphid_file)\n",
"\n",
" cid_graph_map = {}\n",
" for smiles, graph_id in mol_idx_map.items():\n",
" if graph_id in cid_graph_map:\n",
" continue\n",
" graph = smiles2data(smiles)\n",
" cid_graph_map[graph_id] = graph\n",
" torch.save(cid_graph_map, target_file)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# make downstream (retrosynthesis) graphs\n",
"root = 'data/ChEBI-20_data'\n",
"target_file = f'{root}/cid_graph_map.pt'\n",
"\n",
"cid_graph_map = {}\n",
"if not os.path.exists(target_file):\n",
" for mode in ['train', 'validation', 'test']:\n",
" with open(f'{root}/{mode}.txt') as f:\n",
" lines = f.readlines()\n",
" for line in lines[1:]:\n",
" cid, smiles, _ = line.strip().split('\\t', maxsplit=2)\n",
" graph = smiles2data(smiles)\n",
" cid_graph_map[cid] = graph\n",
" torch.save(cid_graph_map, target_file)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pth20v3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.17"
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},
"nbformat": 4,
"nbformat_minor": 2
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