{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import os\n", "import h5py\n", "\n", "def load_from_HDF(fname):\n", " \"\"\"Load data from a HDF5 file to a dictionary.\"\"\"\n", " data = dict()\n", " with h5py.File(fname, 'r') as f:\n", " for key in f:\n", " data[key] = np.asarray(f[key])\n", " if isinstance(data[key][0], np.bytes_):\n", " data[key] = data[key].astype(str)\n", " return data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SMILES 数量: (13221, 1)\n", "10726 13221\n" ] } ], "source": [ "Smiles_union = pd.read_csv('CIGS_smiles_list.csv')\n", "print(\"SMILES 数量:\", Smiles_union.shape)\n", "Smiles_unique = np.unique(Smiles_union)\n", "\n", "print(len(Smiles_unique), len(Smiles_union))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 规范化 SMILES" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "规范化前后的 SMILES 数组:\n", "10726 10726\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[21:21:25] WARNING: not removing hydrogen atom without neighbors\n", "[21:21:25] WARNING: not removing hydrogen atom without neighbors\n", "[21:21:25] WARNING: not removing hydrogen atom without neighbors\n" ] } ], "source": [ "import numpy as np\n", "from rdkit import Chem\n", "\n", "# 假设你的 results_CP['smiles'] 数组如你所示\n", "smiles_array = np.array(Smiles_unique)\n", "\n", "# 存储规范化后的 SMILES\n", "canonical_smiles_list = []\n", "\n", "# 遍历数组中的每一个 SMILES\n", "for byte_smiles in smiles_array:\n", " # Chem.MolFromSmiles() 会处理非规范的 SMILES，并返回一个分子对象\n", " mol = Chem.MolFromSmiles(byte_smiles)\n", "\n", " # 3. 如果解析成功，生成规范 SMILES\n", " if mol is not None:\n", " # Chem.MolToSmiles() 默认生成规范 SMILES\n", " canonical_smiles = Chem.MolToSmiles(mol)\n", " canonical_smiles_list.append(canonical_smiles)\n", " else:\n", " # 如果 SMILES 无效，可以添加 None 或原始字符串\n", " print(f\"警告: 无法解析 SMILES: {byte_smiles}\")\n", " canonical_smiles_list.append(None) # 或者 smiles_str\n", "\n", "# 将列表转换回 NumPy 数组\n", "# dtype='O' 可以存储不同长度的字符串\n", "canonical_smiles_array = np.array(canonical_smiles_list, dtype='O')\n", "\n", "print(\"规范化前后的 SMILES 数组:\")\n", "print(len(smiles_array), len(canonical_smiles_array))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# save canonical_smiles_array to txt file\n", "\n", "canonical_smiles_array = pd.DataFrame(Smiles_unique, columns=['SMILES'])\n", "canonical_smiles_array.to_csv('CIGS_Smiles_list.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate ECFP4 embedding for all smiles\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[21:37:17] WARNING: not removing hydrogen atom without neighbors\n", "[21:37:17] WARNING: not removing hydrogen atom without neighbors\n", "[21:37:17] WARNING: not removing hydrogen atom without neighbors\n" ] } ], "source": [ "from rdkit import Chem\n", "from rdkit.Chem import AllChem\n", "import numpy as np\n", "import pickle\n", "\n", "def smiles_to_ecfp4(smiles_list, radius=2, n_bits=2048):\n", " \"\"\"\n", " 将SMILES列表转换为ECFP4特征向量。\n", "\n", " 参数:\n", " smiles_list (list): SMILES字符串列表。\n", " radius (int): ECFP的半径，默认为2（即ECFP4）。\n", " n_bits (int): 特征向量的长度，默认为2048。\n", "\n", " 返回:\n", " dict: 一个字典，键为SMILES字符串，值为对应的ECFP4特征向量。\n", " \"\"\"\n", " ecfp4_dict = {}\n", " \n", " for smiles in smiles_list:\n", " mol = Chem.MolFromSmiles(smiles)\n", " if mol is not None:\n", " # 替换编码器\n", " ecfp4 = AllChem.GetMorganFingerprintAsBitVect(mol, radius=radius, nBits=n_bits)\n", " ecfp4_dict[smiles] = np.array(ecfp4, dtype=np.float32)\n", " else:\n", " print(\"Error in \", smiles)\n", " # 如果SMILES无法解析为分子，则填充一个全零的向量\n", " ecfp4_dict[smiles] = np.zeros(n_bits, dtype=np.float32)\n", " \n", " return ecfp4_dict\n", "\n", "# 从字典中提取SMILES列表\n", "smiles_list = list(canonical_smiles_array) # 假设idx2smi是一个字典，其值为SMILES字符串\n", "\n", "# 计算ECFP4特征\n", "ecfp4_features_dict = smiles_to_ecfp4(smiles_list)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10726 10726\n" ] } ], "source": [ "print(len(smiles_list), len(ecfp4_features_dict))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# 保存为 pickle 文件\n", "with open('/home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/ECFP4_emb2048.pickle', 'wb') as f:\n", " pickle.dump(ecfp4_features_dict, f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate KGPT embedding for all smiles" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ 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'CC(C)NC(=O)N1[C@@H]2c3ccc(-c4cccc(C(=O)N(C)C)c4)c(=O)n3C[C@H]1[C@H](CO)[C@H]2C(=O)O': array([ 1.7293582 , 1.0055207 , -1.3785439 , ..., -0.18616599,\n", " -0.4220582 , 0.13191678], dtype=float32),\n", " 'CC(C)NC(=O)N1[C@@H]2c3ccc(-c4ccccc4F)c(=O)n3C[C@@H]2[C@@H](CO)[C@@H]1C(=O)NC1CCC1': array([ 1.669677 , 1.2136364 , -0.36254242, ..., -0.08400639,\n", " -0.7146096 , -0.13481204], dtype=float32),\n", " 'CC(C)NC(=O)N1[C@@H]2c3cccc(=O)n3C[C@@H]2[C@@H](CO)[C@@H]1C(=O)N1CCc2ccccc2C1': array([ 2.480095 , 1.3386377 , -0.8075468 , ..., 0.07403517,\n", " -0.76204103, 0.02629161], dtype=float32),\n", " ...}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pickle, h5py\n", "import numpy as np\n", "import pandas as pd\n", "\n", "with open('./Molecule_encoder/KPGT_emb2304.pickle', 'rb') as f:\n", " KPGT_emb2304 = pickle.load(f)\n", "KPGT_emb2304" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate InfoAlign embedding for all smiles" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# pip install infoalign\n", "\n", "# infoalign_predict --input CIGS_Smiles_unique.csv \\\n", "# --output InfoAlign_output.npy \\\n", "# --output-to-input-column # Adds the representation as an additional column in the input CSV\n", "\n", "# 是否存在数据泄露，因为这个是基于表征学习的" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import csv\n", "import numpy as np\n", "import pickle\n", "\n", "# CSV文件路径\n", "csv_path = 'CIGS_Smiles_unique.csv'\n", "# NPY文件路径\n", "npy_path = 'InfoAlign_output.npy'\n", "\n", "# 初始化一个空字典来存储SMILES和对应的表征\n", "smiles_to_representation = {}\n", "\n", "# 读取NPY文件\n", "info_align_output = np.load(npy_path)\n", "\n", "# 读取CSV文件并同时替换字典中的值\n", "with open(csv_path, mode='r', newline='', encoding='utf-8') as csvfile:\n", " reader = csv.DictReader(csvfile)\n", " for idx, row in enumerate(reader):\n", " smiles = row['SMILES']\n", " # 直接从info_align_output中获取对应的数值\n", " representation = info_align_output[idx]\n", " smiles_to_representation[smiles] = representation\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10726" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(smiles_to_representation)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "字典已成功保存到 /home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/InfoAlign_emb300.pickle\n" ] } ], "source": [ "# 保存字典为pickle文件\n", "pickle_path = '/home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/InfoAlign_emb300.pickle'\n", "with open(pickle_path, 'wb') as pickle_file:\n", " pickle.dump(smiles_to_representation, pickle_file)\n", "\n", "print(f\"字典已成功保存到 {pickle_path}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate ChemBERTa-2 embedding for all smiles" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of RobertaModel were not initialized from the model checkpoint at DeepChem/ChemBERTa-77M-MLM and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ], "source": [ "from transformers import AutoModel, AutoTokenizer\n", "model = AutoModel.from_pretrained(\"DeepChem/ChemBERTa-77M-MLM\") # 77M SMILES（完整PubChem数据集)\n", "tokenizer = AutoTokenizer.from_pretrained(\"DeepChem/ChemBERTa-77M-MLM\")\n", "inputs = tokenizer(\"CCO\", return_tensors=\"pt\")\n", "outputs = model(**inputs) # 嵌入位于 outputs.last_hidden_state" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([1, 5, 384])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outputs.last_hidden_state.shape" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10726" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(Smiles_unique)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of RobertaModel were not initialized from the model checkpoint at DeepChem/ChemBERTa-77M-MLM and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", " 98%|█████████▊| 328/336 [00:02<00:00, 148.55it/s][19:42:56] WARNING: not removing hydrogen atom without neighbors\n", "[19:42:56] WARNING: not removing hydrogen atom without neighbors\n", "[19:42:56] WARNING: not removing hydrogen atom without neighbors\n", "100%|██████████| 336/336 [00:02<00:00, 142.69it/s]\n" ] } ], "source": [ "import pickle\n", "from transformers import AutoModel, AutoTokenizer\n", "import torch\n", "import numpy as np\n", "from rdkit import Chem\n", "from tqdm import tqdm # 可选：用于进度条\n", "\n", "def smiles_to_chemberta2(smiles_list, model_name=\"DeepChem/ChemBERTa-77M-MLM\", batch_size=32):\n", " \"\"\"\n", " 将SMILES列表转换为ChemBERTa-2的嵌入向量（CLS token的隐藏状态）。\n", " \n", " 参数:\n", " smiles_list (list): SMILES字符串列表\n", " model_name (str): Hugging Face模型ID，默认为ChemBERTa-2的MLM版本\n", " batch_size (int): 批处理大小，根据GPU内存调整\n", " \n", " 返回:\n", " dict: 键为SMILES，值为768维嵌入向量（numpy数组）\n", " \"\"\"\n", " # 加载模型和tokenizer\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " model = AutoModel.from_pretrained(model_name)\n", " model.eval() # 切换到推理模式\n", " \n", " # 检查CUDA可用性\n", " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " model.to(device)\n", " \n", " chemberta2_dict = {}\n", " \n", " # 分批处理SMILES\n", " for i in tqdm(range(0, len(smiles_list), batch_size)):\n", " batch_smiles = smiles_list[i:i+batch_size]\n", " valid_indices = []\n", " batch_inputs = []\n", " \n", " # 过滤无效SMILES并生成输入\n", " for idx, smiles in enumerate(batch_smiles):\n", " mol = Chem.MolFromSmiles(smiles)\n", " if mol is not None:\n", " valid_indices.append(idx)\n", " batch_inputs.append(smiles)\n", " else:\n", " print(f\"Invalid SMILES: {smiles}\")\n", " \n", " if not batch_inputs:\n", " continue\n", " \n", " # Tokenize并移动到设备\n", " inputs = tokenizer(\n", " batch_inputs, \n", " return_tensors=\"pt\", \n", " padding=True, \n", " truncation=True, \n", " max_length=512\n", " ).to(device)\n", " \n", " # 生成嵌入\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", " embeddings = outputs.last_hidden_state[:, 0, :] # 取CLS token的嵌入\n", " \n", " # 将有效结果存入字典\n", " for j, idx in enumerate(valid_indices):\n", " chemberta2_dict[batch_smiles[idx]] = embeddings[j].cpu().numpy()\n", " \n", " # 处理无效SMILES（填充零向量）\n", " for idx, smiles in enumerate(batch_smiles):\n", " if idx not in valid_indices:\n", " chemberta2_dict[smiles] = np.zeros(768, dtype=np.float32)\n", " \n", " return chemberta2_dict\n", "\n", "# 使用示例\n", "smiles_list = list(Smiles_unique) # 假设idx2smi是SMILES字典\n", "chemberta2_features_dict = smiles_to_chemberta2(smiles_list)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10726\n" ] } ], "source": [ "print(len(chemberta2_features_dict))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "字典已成功保存到 /home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/ChemBERTa2_emb384.pickle\n" ] } ], "source": [ "# 保存字典为pickle文件\n", "pickle_path = '/home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/ChemBERTa2_emb384.pickle'\n", "with open(pickle_path, 'wb') as pickle_file:\n", " pickle.dump(chemberta2_features_dict, pickle_file)\n", "\n", "print(f\"字典已成功保存到 {pickle_path}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate MolT5 embedding for all smiles" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 768])\n" ] } ], "source": [ "from transformers import T5ForConditionalGeneration, AutoTokenizer\n", "model = T5ForConditionalGeneration.from_pretrained(\"laituan245/molt5-base\")\n", "tokenizer = AutoTokenizer.from_pretrained(\"laituan245/molt5-base\")\n", "inputs = tokenizer(\"CCO\", return_tensors=\"pt\")\n", "embeddings = model.encoder(**inputs).last_hidden_state.mean(dim=1) # 平均池化\n", "print(embeddings.shape)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 168/168 [00:11<00:00, 14.54it/s]\n" ] } ], "source": [ "import pickle\n", "from transformers import T5ForConditionalGeneration, AutoTokenizer\n", "import torch\n", "import numpy as np\n", "from rdkit import Chem\n", "from tqdm import tqdm\n", "\n", "def smiles_to_molt5(smiles_list, model_name=\"laituan245/molt5-base\", batch_size=32, pooling=\"mean\"):\n", " \"\"\"\n", " 使用 MolT5 生成 SMILES 的嵌入向量（基于 encoder 的隐藏状态）\n", " \n", " 参数:\n", " smiles_list (list): SMILES 字符串列表\n", " model_name (str): Hugging Face 模型 ID，默认为 MolT5-base\n", " batch_size (int): 批处理大小，根据 GPU 内存调整\n", " pooling (str): 池化方法，可选 \"mean\"（平均池化）或 \"cls\"（取 CLS token）\n", " \n", " 返回:\n", " dict: 键为 SMILES，值为嵌入向量（numpy 数组）\n", " \"\"\"\n", " # 加载 MolT5 的 tokenizer 和模型\n", " model = T5ForConditionalGeneration.from_pretrained(model_name)\n", " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", " model.eval()\n", " \n", " # 设备设置\n", " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " model.to(device)\n", " \n", " molt5_dict = {}\n", " \n", " # 分批处理\n", " for i in tqdm(range(0, len(smiles_list), batch_size)):\n", " batch_smiles = smiles_list[i:i+batch_size]\n", " valid_indices = []\n", " batch_inputs = []\n", " \n", " # 过滤无效 SMILES\n", " for idx, smiles in enumerate(batch_smiles):\n", " mol = Chem.MolFromSmiles(smiles)\n", " if mol is not None:\n", " valid_indices.append(idx)\n", " batch_inputs.append(smiles)\n", " else:\n", " print(f\"Invalid SMILES: {smiles}\")\n", " molt5_dict[smiles] = np.zeros(model.config.d_model, dtype=np.float32) # 填充零向量\n", " \n", " if not batch_inputs:\n", " continue\n", " \n", " # Tokenize 并生成嵌入\n", " inputs = tokenizer(\n", " batch_inputs,\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=True,\n", " max_length=512\n", " ).to(device)\n", " \n", " with torch.no_grad():\n", " # 获取 encoder 的隐藏状态（形状: [batch_size, seq_len, hidden_dim]）\n", " encoder_outputs = model.encoder(**inputs)\n", " hidden_states = encoder_outputs.last_hidden_state\n", " \n", " # 池化策略\n", " if pooling == \"mean\":\n", " embeddings = hidden_states.mean(dim=1) # 平均池化\n", " elif pooling == \"cls\":\n", " embeddings = hidden_states[:, 0, :] # CLS token\n", " else:\n", " raise ValueError(\"pooling 必须是 'mean' 或 'cls'\")\n", " \n", " # 存储结果\n", " for j, idx in enumerate(valid_indices):\n", " molt5_dict[batch_smiles[idx]] = embeddings[j].cpu().numpy()\n", " \n", " return molt5_dict\n", "\n", "# 使用示例\n", "smiles_list = list(Smiles_unique) # 假设 idx2smi 是 SMILES 字典\n", "molt5_embeddings = smiles_to_molt5(smiles_list, batch_size=64, pooling=\"mean\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10726\n" ] } ], "source": [ "print(len(molt5_embeddings))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "字典已成功保存到 /home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/molt5_emb768.pickle\n" ] } ], "source": [ "# 保存字典为pickle文件\n", "pickle_path = '/home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/molt5_emb768.pickle'\n", "with open(pickle_path, 'wb') as pickle_file:\n", " pickle.dump(molt5_embeddings, pickle_file)\n", "\n", "print(f\"字典已成功保存到 {pickle_path}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate Chemprop embedding for all smiles" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# git clone https://github.com/chemprop/chemprop.git\n", "# https://chemprop.readthedocs.io/en/main/installation.html\n", "\n", "# chemprop fingerprint --test-path ./CIGS_Smiles_unique.csv --model-path /home/bob/boom/VCBench/Molecule_encoder/chemprop/tests/data/example_model_v2_regression_mol.ckpt --output fps.csv --ffn-block-index -2" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10726" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import csv\n", "import numpy as np\n", "\n", "# CSV文件路径\n", "csv1_path = 'CIGS_Smiles_unique.csv'\n", "csv2_path = 'fps_0.csv'\n", "\n", "# 初始化一个空字典来存储SMILES和对应的表征\n", "smiles_to_representation = {}\n", "\n", "# 读取第一个CSV文件（SMILES信息）\n", "smiles_list = []\n", "with open(csv1_path, mode='r', newline='', encoding='utf-8') as csvfile:\n", " reader = csv.DictReader(csvfile)\n", " for row in reader:\n", " smiles = row['SMILES'] # 假设CSV文件中列名为'SMILES'\n", " smiles_list.append(smiles)\n", "\n", "# 读取第二个CSV文件（表征信息），跳过第一行\n", "representation_list = []\n", "with open(csv2_path, mode='r', newline='', encoding='utf-8') as csvfile:\n", " reader = csv.reader(csvfile)\n", " next(reader) # 跳过第一行\n", " for row in reader:\n", " # 将字符串转换为浮点数\n", " representation = [np.float32(x) for x in row] # 假设表征信息是整行数据\n", " representation_list.append(representation)\n", "\n", "# 将列表转换为numpy数组\n", "representation_array = np.array(representation_list)\n", "\n", "# 将SMILES和对应的表征合并到字典中\n", "for smiles, representation in zip(smiles_list, representation_array):\n", " smiles_to_representation[smiles] = representation\n", "\n", "len(smiles_to_representation)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "字典已成功保存到 /home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/Chemprop_emb300.pickle\n" ] } ], "source": [ "import pickle\n", "# 保存字典为pickle文件\n", "pickle_path = '/home/bob/boom/VCBench/Molecule_encoder/embeddings/CIGS/Chemprop_emb300.pickle'\n", "with open(pickle_path, 'wb') as pickle_file:\n", " pickle.dump(smiles_to_representation, pickle_file)\n", "\n", "print(f\"字典已成功保存到 {pickle_path}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate MolCLR embedding for all smiles\n", "\n", "## see /home/bob/boom/VCBench/Molecule_encoder/MolCLR/Get_emd.ipynb\n", "\n", "### We use GIN model, which has a better performance in their paper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate Mole-BERT embedding for all smiles\n", "\n", "## see /home/bob/boom/VCBench/Molecule_encoder/Mole-BERT/Get_emd.ipynb\n", "\n", "### We use GNN_graphpred model, which has a better performance in their paper" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D UniMol" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import pickle, h5py\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# with open('./LINCS2020/idx2smi.pickle', 'rb') as f:\n", "# idx2smi = pickle.load(f)\\\n", "\n", "# unique_smiles = list(idx2smi.values()) # 假设idx2smi是一个字典，其值为SMILES字符串\n", "# len(unique_smiles)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-09-06 13:12:59 | unimol_tools/models/unimol.py | 136 | INFO | Uni-Mol Tools | Loading pretrained weights from /home/bob/anaconda3/envs/boom/lib/python3.11/site-packages/unimol_tools/weights/mol_pre_all_h_220816.pt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "开始生成10726个SMILES的嵌入表示...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "处理批次: 0%| | 0/168 [00:00