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"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"import selfies as sf\n",
"from rdkit import Chem\n",
"from typing import Optional\n",
"import numpy as np\n",
"import py3Dmol\n",
"from rdkit import Chem, DataStructs\n",
"from rdkit.Chem import AllChem\n",
"import torch\n",
"\n",
"def smiles_to_3d(smiles_list, width=400, height=300):\n",
" # Visualize the 3D structure using py3Dmol\n",
" view = py3Dmol.view(width=width, height=height)\n",
" for smiles in smiles_list:\n",
" # Generate the RDKit molecule object\n",
" mol = Chem.MolFromSmiles(smiles)\n",
" if mol is None:\n",
" raise ValueError(\"Invalid SMILES string\")\n",
"\n",
" # Add hydrogens to the molecule\n",
" mol = Chem.AddHs(mol)\n",
"\n",
" # Generate 3D coordinates\n",
" AllChem.EmbedMolecule(mol, randomSeed=42)\n",
" AllChem.UFFOptimizeMolecule(mol)\n",
"\n",
" # Generate the 3D structure in the form of a pdb string\n",
" pdb = Chem.MolToPDBBlock(mol)\n",
" view.addModel(pdb, 'pdb')\n",
" view.setStyle({'stick': {}})\n",
" view.zoomTo()\n",
" return view\n",
"\n",
" \n",
"# Load the checkpoint and the tokenizer\n",
"checkpoint_path = \"lamthuy/SelfiesGen\"\n",
"model = AutoModelForCausalLM.from_pretrained(checkpoint_path)\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9b7066a4-6637-4d45-a0d9-3cc5e2ca0409",
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"\tviewer_1753263966683991.addModel(\"HETATM 1 C1 UNL 1 -2.975 -2.217 0.190 1.00 0.00 C \\nHETATM 2 C2 UNL 1 -2.096 -1.150 -0.368 1.00 0.00 C \\nHETATM 3 O1 UNL 1 -2.392 -0.615 -1.471 1.00 0.00 O \\nHETATM 4 O2 UNL 1 -0.898 -0.839 0.288 1.00 0.00 O \\nHETATM 5 C3 UNL 1 -0.095 0.284 0.025 1.00 0.00 C \\nHETATM 6 C4 UNL 1 -0.671 1.501 -0.391 1.00 0.00 C \\nHETATM 7 C5 UNL 1 0.129 2.617 -0.638 1.00 0.00 C \\nHETATM 8 C6 UNL 1 1.509 2.540 -0.463 1.00 0.00 C \\nHETATM 9 C7 UNL 1 2.094 1.348 -0.032 1.00 0.00 C \\nHETATM 10 C8 UNL 1 1.307 0.209 0.223 1.00 0.00 C \\nHETATM 11 C9 UNL 1 1.972 -1.038 0.682 1.00 0.00 C \\nHETATM 12 O3 UNL 1 1.307 -2.087 0.898 1.00 0.00 O \\nHETATM 13 O4 UNL 1 3.351 -1.062 0.878 1.00 0.00 O \\nHETATM 14 H1 UNL 1 -4.038 -1.995 -0.040 1.00 0.00 H \\nHETATM 15 H2 UNL 1 -2.850 -2.271 1.291 1.00 0.00 H \\nHETATM 16 H3 UNL 1 -2.699 -3.195 -0.257 1.00 0.00 H \\nHETATM 17 H4 UNL 1 -1.742 1.599 -0.498 1.00 0.00 H \\nHETATM 18 H5 UNL 1 -0.324 3.547 -0.957 1.00 0.00 H \\nHETATM 19 H6 UNL 1 2.126 3.408 -0.653 1.00 0.00 H \\nHETATM 20 H7 UNL 1 3.168 1.322 0.102 1.00 0.00 H \\nHETATM 21 H8 UNL 1 3.818 -1.905 1.190 1.00 0.00 H \\nCONECT 1 2 14 15 16\\nCONECT 2 3 3 4\\nCONECT 4 5\\nCONECT 5 6 6 10\\nCONECT 6 7 17\\nCONECT 7 8 8 18\\nCONECT 8 9 19\\nCONECT 9 10 10 20\\nCONECT 10 11\\nCONECT 11 12 12 13\\nCONECT 13 21\\nEND\\n\",\"pdb\");\n",
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"text/plain": [
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],
"source": [
"# Given a SMILES, get its fingerpint\n",
"smiles = \"CC(=O)OC1=CC=CC=C1C(=O)O\"\n",
"smiles_to_3d([smiles])"
]
},
{
"cell_type": "code",
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"id": "05f9bf21-c998-4d63-870e-c1033ff91b31",
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"name": "stderr",
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"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[C][C][=Branch1][C][=O][O][C][=C][C][=C][C][=C][Ring1][=Branch1][C][=Branch1][C][=S][O][SEP]\n",
"[C][C][=Branch1][C][=O][O][C][=C][C][=C][C][=C][Ring1][=Branch1][C][=Branch1][C][=NH2+1][O]\n",
"CC(=O)OC1=CC=CC=C1C(=[NH2+1])O\n"
]
},
{
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" </div>\n",
"<script>\n",
"\n",
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"\tviewer_1753264053659028.addModel(\"HETATM 1 C1 UNL 1 2.411 -2.582 -1.327 1.00 0.00 C \\nHETATM 2 C2 UNL 1 1.777 -1.233 -1.311 1.00 0.00 C \\nHETATM 3 O1 UNL 1 1.971 -0.444 -2.275 1.00 0.00 O \\nHETATM 4 O2 UNL 1 0.887 -0.908 -0.278 1.00 0.00 O \\nHETATM 5 C3 UNL 1 0.406 0.380 0.005 1.00 0.00 C \\nHETATM 6 C4 UNL 1 1.187 1.525 -0.250 1.00 0.00 C \\nHETATM 7 C5 UNL 1 0.691 2.798 0.033 1.00 0.00 C \\nHETATM 8 C6 UNL 1 -0.580 2.947 0.586 1.00 0.00 C \\nHETATM 9 C7 UNL 1 -1.357 1.822 0.866 1.00 0.00 C \\nHETATM 10 C8 UNL 1 -0.876 0.529 0.582 1.00 0.00 C \\nHETATM 11 C9 UNL 1 -1.702 -0.662 0.905 1.00 0.00 C \\nHETATM 12 N1 UNL 1 -2.996 -0.640 0.776 1.00 0.00 N1+\\nHETATM 13 O3 UNL 1 -1.073 -1.826 1.349 1.00 0.00 O \\nHETATM 14 H1 UNL 1 1.780 -3.281 -1.914 1.00 0.00 H \\nHETATM 15 H2 UNL 1 2.512 -2.964 -0.290 1.00 0.00 H \\nHETATM 16 H3 UNL 1 3.421 -2.524 -1.787 1.00 0.00 H \\nHETATM 17 H4 UNL 1 2.190 1.435 -0.643 1.00 0.00 H \\nHETATM 18 H5 UNL 1 1.299 3.672 -0.166 1.00 0.00 H \\nHETATM 19 H6 UNL 1 -0.957 3.936 0.813 1.00 0.00 H \\nHETATM 20 H7 UNL 1 -2.325 1.965 1.329 1.00 0.00 H \\nHETATM 21 H8 UNL 1 -3.567 -1.482 1.010 1.00 0.00 H \\nHETATM 22 H9 UNL 1 -3.499 0.198 0.408 1.00 0.00 H \\nHETATM 23 H10 UNL 1 -1.599 -2.662 1.576 1.00 0.00 H \\nCONECT 1 2 14 15 16\\nCONECT 2 3 3 4\\nCONECT 4 5\\nCONECT 5 6 6 10\\nCONECT 6 7 17\\nCONECT 7 8 8 18\\nCONECT 8 9 19\\nCONECT 9 10 10 20\\nCONECT 10 11\\nCONECT 11 12 12 13\\nCONECT 12 21 22\\nCONECT 13 23\\nEND\\n\",\"pdb\");\n",
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"source": [
"s = sf.encoder(smiles)\n",
"s = s + \"[SEP]\"\n",
"print(s)\n",
"input_ids = tokenizer.encode(s, return_tensors=\"pt\")\n",
"n = input_ids.size(1)\n",
"# Generate output sequence\n",
"output_ids = model.generate(input_ids, max_length=128, num_beams=5, num_return_sequences=5,\n",
" early_stopping=True)\n",
"output = tokenizer.decode(output_ids[1][n:], skip_special_tokens=True)\n",
"print(output)\n",
"smiles = sf.decoder(output)\n",
"print(smiles)\n",
"smiles_to_3d([smiles])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "dbe6cebd-c7d5-4da9-aac0-114f232cf147",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[C][C][=Branch1][C][=O][N][C][=C][C][=N][C][=C][Ring1][=Branch1][C][=Branch1][C][=S][O-1]\n",
"CC(=O)NC1=CC=NC=C1C(=S)[O-1]\n"
]
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"text/html": [
"<div id=\"3dmolviewer_17532640697518232\" style=\"position: relative; width: 400px; height: 300px;\">\n",
" <p id=\"3dmolwarning_17532640697518232\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason. Please check your browser console for error messages.<br></p>\n",
" </div>\n",
"<script>\n",
"\n",
"var loadScriptAsync = function(uri){\n",
" return new Promise((resolve, reject) => {\n",
" //this is to ignore the existence of requirejs amd\n",
" var savedexports, savedmodule;\n",
" if (typeof exports !== 'undefined') savedexports = exports;\n",
" else exports = {}\n",
" if (typeof module !== 'undefined') savedmodule = module;\n",
" else module = {}\n",
"\n",
" var tag = document.createElement('script');\n",
" tag.src = uri;\n",
" tag.async = true;\n",
" tag.onload = () => {\n",
" exports = savedexports;\n",
" module = savedmodule;\n",
" resolve();\n",
" };\n",
" var firstScriptTag = document.getElementsByTagName('script')[0];\n",
" firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
"});\n",
"};\n",
"\n",
"if(typeof $3Dmolpromise === 'undefined') {\n",
"$3Dmolpromise = null;\n",
" $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.4.2/3Dmol-min.js');\n",
"}\n",
"\n",
"var viewer_17532640697518232 = null;\n",
"var warn = document.getElementById(\"3dmolwarning_17532640697518232\");\n",
"if(warn) {\n",
" warn.parentNode.removeChild(warn);\n",
"}\n",
"$3Dmolpromise.then(function() {\n",
"viewer_17532640697518232 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17532640697518232\"),{backgroundColor:\"white\"});\n",
"viewer_17532640697518232.zoomTo();\n",
"\tviewer_17532640697518232.addModel(\"HETATM 1 C1 UNL 1 3.341 0.506 -0.349 1.00 0.00 C \\nHETATM 2 C2 UNL 1 1.994 -0.109 -0.554 1.00 0.00 C \\nHETATM 3 O1 UNL 1 1.839 -0.974 -1.458 1.00 0.00 O \\nHETATM 4 N1 UNL 1 0.876 0.346 0.221 1.00 0.00 N \\nHETATM 5 C3 UNL 1 -0.424 -0.266 0.167 1.00 0.00 C \\nHETATM 6 C4 UNL 1 -0.537 -1.665 0.219 1.00 0.00 C \\nHETATM 7 C5 UNL 1 -1.798 -2.258 0.278 1.00 0.00 C \\nHETATM 8 N2 UNL 1 -2.913 -1.485 0.302 1.00 0.00 N \\nHETATM 9 C6 UNL 1 -2.844 -0.130 0.260 1.00 0.00 C \\nHETATM 10 C7 UNL 1 -1.602 0.521 0.177 1.00 0.00 C \\nHETATM 11 C8 UNL 1 -1.585 1.998 0.085 1.00 0.00 C \\nHETATM 12 S1 UNL 1 -0.600 2.783 -0.963 1.00 0.00 S \\nHETATM 13 O2 UNL 1 -2.485 2.742 0.838 1.00 0.00 O1-\\nHETATM 14 H1 UNL 1 3.531 0.642 0.736 1.00 0.00 H \\nHETATM 15 H2 UNL 1 4.133 -0.148 -0.770 1.00 0.00 H \\nHETATM 16 H3 UNL 1 3.376 1.493 -0.854 1.00 0.00 H \\nHETATM 17 H4 UNL 1 1.005 1.188 0.826 1.00 0.00 H \\nHETATM 18 H5 UNL 1 0.344 -2.292 0.249 1.00 0.00 H \\nHETATM 19 H6 UNL 1 -1.889 -3.335 0.323 1.00 0.00 H \\nHETATM 20 H7 UNL 1 -3.764 0.442 0.270 1.00 0.00 H \\nCONECT 1 2 14 15 16\\nCONECT 2 3 3 4\\nCONECT 4 5 17\\nCONECT 5 6 6 10\\nCONECT 6 7 18\\nCONECT 7 8 8 19\\nCONECT 8 9\\nCONECT 9 10 10 20\\nCONECT 10 11\\nCONECT 11 12 12 13\\nEND\\n\",\"pdb\");\n",
"\tviewer_17532640697518232.setStyle({\"stick\": {}});\n",
"\tviewer_17532640697518232.zoomTo();\n",
"viewer_17532640697518232.render();\n",
"});\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<py3Dmol.view at 0x11fd5a780>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_ids[0][5] = tokenizer.mask_token_id\n",
"input_ids[0][9] = tokenizer.mask_token_id\n",
"input_ids[0][18] = tokenizer.mask_token_id\n",
"input_ids[0][11] = tokenizer.mask_token_id\n",
"# Generate output sequence\n",
"output_ids = model.generate(input_ids, max_length=128, num_beams=5, num_return_sequences=5,\n",
" early_stopping=True)\n",
"output = tokenizer.decode(output_ids[1][n:], skip_special_tokens=True)\n",
"print(output)\n",
"smiles = sf.decoder(output)\n",
"print(smiles)\n",
"smiles_to_3d([smiles])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f696bb9c-2870-4b0b-9b62-1623411e5df6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|