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"cells": [
{
"cell_type": "markdown",
"id": "bead31c0",
"metadata": {},
"source": [
"# 先去下载好数据： https://cigs.iomicscloud.com/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0db2da68",
"metadata": {},
"outputs": [],
"source": [
"import pickle, h5py\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "02cf9b84",
"metadata": {},
"source": [
"## 转成 parquet 格式更方便处理"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "006f7147",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import glob\n",
"\n",
"# 定义要处理的根目录\n",
"base_path = \"/data/boom/CIGS/\"\n",
"\n",
"# 使用 glob 找到所有以 _Counts.xlsx 结尾的文件\n",
"count_files = glob.glob(os.path.join(base_path, \"*_Counts.xlsx\"))\n",
"\n",
"if not count_files:\n",
" print(f\"在路径 '{base_path}' 下没有找到任何以 '_Counts.xlsx' 结尾的文件。\")\n",
"else:\n",
" print(f\"找到 {len(count_files)} 个匹配的文件，开始批量处理...\")\n",
"\n",
"# 循环处理每个文件\n",
"for count_path in count_files:\n",
" try:\n",
" print(f\"\\n--- 正在处理文件: {count_path} ---\")\n",
"\n",
" # 1. 处理 Counts 文件\n",
" counts = pd.read_excel(count_path, engine=\"openpyxl\", header=1)\n",
" # 清理空行/列\n",
" counts = counts.dropna(axis=1, how=\"all\").dropna(axis=0, how=\"all\")\n",
"\n",
" # 规范化首列\n",
" id_col = counts.columns[0]\n",
" if str(id_col).lower() != \"sample_unique_id\":\n",
" counts = counts.rename(columns={id_col: \"Sample_unique_id\"})\n",
" counts[\"Sample_unique_id\"] = counts[\"Sample_unique_id\"].astype(str)\n",
"\n",
" # 构建 Parquet 文件名\n",
" parquet_path = count_path.replace(\"_Counts.xlsx\", \"_Counts.parquet\")\n",
" counts.to_parquet(parquet_path, index=False)\n",
" print(f\"成功处理并保存 Counts 文件到: {parquet_path}\")\n",
"\n",
" # 2. 处理对应的 MetaData 文件\n",
" meta_path = count_path.replace(\"_Counts.xlsx\", \"_MetaData.xlsx\")\n",
" if os.path.exists(meta_path):\n",
" meta = pd.read_excel(meta_path, engine=\"openpyxl\", header=1)\n",
" meta = meta.dropna(axis=1, how=\"all\").dropna(axis=0, how=\"all\")\n",
"\n",
" parquet_meta_path = meta_path.replace(\"_MetaData.xlsx\", \"_MetaData.parquet\")\n",
" meta.to_parquet(parquet_meta_path, index=False)\n",
" print(f\"成功处理并保存 MetaData 文件到: {parquet_meta_path}\")\n",
" else:\n",
" print(f\"警告: 未找到对应的 MetaData 文件: {meta_path}，跳过处理。\")\n",
"\n",
" except Exception as e:\n",
" print(f\"错误: 处理文件 '{count_path}' 时出错: {e}\")\n",
" print(\"跳过此文件，继续处理下一个。\")\n",
"\n",
"print(\"\\n所有文件处理完毕。\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2514cecc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"合并后的数据集形状: (13221, 7)\n"
]
},
{
"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>Compound name</th>\n",
" <th>Catalog Number</th>\n",
" <th>CAS Number</th>\n",
" <th>MOA</th>\n",
" <th>Clinical Information</th>\n",
" <th>Approved Type</th>\n",
" <th>Catalog Number.1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Pirozadil</td>\n",
" <td>HY_100144</td>\n",
" <td>54110-25-7</td>\n",
" <td>Others</td>\n",
" <td>No Development Reported</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NKL 22</td>\n",
" <td>HY_100384</td>\n",
" <td>537034-15-4</td>\n",
" <td>HDAC</td>\n",
" <td>No Development Reported</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Toll-like receptor modulator</td>\n",
" <td>HY_10018</td>\n",
" <td>926927-42-6</td>\n",
" <td>Toll-like Receptor (TLR)</td>\n",
" <td>No Development Reported</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Lu AF21934</td>\n",
" <td>HY_100366</td>\n",
" <td>1445605-23-1</td>\n",
" <td>mGluR</td>\n",
" <td>No Development Reported</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Vonoprazan</td>\n",
" <td>HY_100007</td>\n",
" <td>881681-00-1</td>\n",
" <td>Proton Pump</td>\n",
" <td>Launched</td>\n",
" <td>FDA; Other Countries</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13216</th>\n",
" <td>Methoxyeugeno l\\n4-O-rhamnosyl(\\n1→2)glucoside</td>\n",
" <td>Cpd1991</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>903519-86-8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13217</th>\n",
" <td>(2S)-1-O-β-D-\\nglucopyranosyl-\\n2-O-acetyl-3-O...</td>\n",
" <td>Cpd1992</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13218</th>\n",
" <td>Manninotriose</td>\n",
" <td>Cpd1998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13382-86-0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13219</th>\n",
" <td>1-Methylhydant\\noin</td>\n",
" <td>Cpd1999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>616-04-6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13220</th>\n",
" <td>Agrimol B</td>\n",
" <td>Cpd2000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>55576-66-4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13221 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Compound name Catalog Number \\\n",
"0 Pirozadil HY_100144 \n",
"1 NKL 22 HY_100384 \n",
"2 Toll-like receptor modulator HY_10018 \n",
"3 Lu AF21934 HY_100366 \n",
"4 Vonoprazan HY_100007 \n",
"... ... ... \n",
"13216 Methoxyeugeno l\\n4-O-rhamnosyl(\\n1→2)glucoside Cpd1991 \n",
"13217 (2S)-1-O-β-D-\\nglucopyranosyl-\\n2-O-acetyl-3-O... Cpd1992 \n",
"13218 Manninotriose Cpd1998 \n",
"13219 1-Methylhydant\\noin Cpd1999 \n",
"13220 Agrimol B Cpd2000 \n",
"\n",
" CAS Number MOA Clinical Information \\\n",
"0 54110-25-7 Others No Development Reported \n",
"1 537034-15-4 HDAC No Development Reported \n",
"2 926927-42-6 Toll-like Receptor (TLR) No Development Reported \n",
"3 1445605-23-1 mGluR No Development Reported \n",
"4 881681-00-1 Proton Pump Launched \n",
"... ... ... ... \n",
"13216 NaN NaN NaN \n",
"13217 NaN NaN NaN \n",
"13218 NaN NaN NaN \n",
"13219 NaN NaN NaN \n",
"13220 NaN NaN NaN \n",
"\n",
" Approved Type Catalog Number.1 \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 FDA; Other Countries NaN \n",
"... ... ... \n",
"13216 NaN 903519-86-8 \n",
"13217 NaN NaN \n",
"13218 NaN 13382-86-0 \n",
"13219 NaN 616-04-6 \n",
"13220 NaN 55576-66-4 \n",
"\n",
"[13221 rows x 7 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"cas_csv = \"/data/boom/CIGS/Drug_infoM.xlsx\"\n",
"\n",
"# 读取两个sheet，假设第二个sheet名为\"Supplementary Table 4\"\n",
"sheet1 = pd.read_excel(cas_csv, sheet_name=\"Supplementary Table 3\", header=1)\n",
"sheet2 = pd.read_excel(cas_csv, sheet_name=\"Supplementary Table 4\", header=1) # 替换为实际的第二个sheet名\n",
"\n",
"# 合并两个DataFrame\n",
"merged_df = pd.concat([sheet1, sheet2], ignore_index=True)\n",
"\n",
"# 查看合并结果\n",
"print(\"合并后的数据集形状:\", merged_df.shape)\n",
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "05c9cbb1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"合并后的数据集形状: (13221, 7)\n",
"转换完成，已保存为 merged_with_smiles.csv\n"
]
}
],
"source": [
"import requests\n",
"import pandas as pd\n",
"import time\n",
"\n",
"def cas_to_smiles_single(cas, sleep_time=0.2):\n",
" \"\"\"\n",
" 输入: 单个CAS号\n",
" 输出: (cid, smiles)\n",
" \"\"\"\n",
" try:\n",
" # 通过 PubChem 查询 CID\n",
" url_cid = f\"https://pubchem.ncbi.nlm.nih.gov/rest/pug/substance/name/{cas}/cids/TXT\"\n",
" r_cid = requests.get(url_cid)\n",
" if r_cid.status_code != 200 or not r_cid.text.strip():\n",
" return None, None\n",
" cid = r_cid.text.strip().split(\"\\n\")[0]\n",
"\n",
" # 通过 CID 获取 canonical SMILES\n",
" url_smiles = f\"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/property/CanonicalSMILES/TXT\"\n",
" r_smiles = requests.get(url_smiles)\n",
" if r_smiles.status_code != 200 or not r_smiles.text.strip():\n",
" return cid, None\n",
" smiles = r_smiles.text.strip()\n",
"\n",
" time.sleep(sleep_time) # 防止被限流\n",
" return cid, smiles\n",
" except:\n",
" return None, None\n",
"\n",
"\n",
"# ================= 主程序 =================\n",
"if __name__ == \"__main__\":\n",
" cas_csv = \"/data/boom/CIGS/Drug_infoM.xlsx\"\n",
"\n",
" # 读取两个sheet\n",
" sheet1 = pd.read_excel(cas_csv, sheet_name=\"Supplementary Table 3\", header=1)\n",
" sheet2 = pd.read_excel(cas_csv, sheet_name=\"Supplementary Table 4\", header=1)\n",
"\n",
" # 合并\n",
" merged_df = pd.concat([sheet1, sheet2], ignore_index=True)\n",
" print(\"合并后的数据集形状:\", merged_df.shape)\n",
"\n",
" # 遍历 CAS 列，生成新列\n",
" smiles_list, cid_list = [], []\n",
" for cas in merged_df[\"CAS Number\"]:\n",
" cid, smiles = cas_to_smiles_single(str(cas))\n",
" cid_list.append(cid)\n",
" smiles_list.append(smiles)\n",
"\n",
" merged_df[\"PubChem_CID\"] = cid_list\n",
" merged_df[\"SMILES\"] = smiles_list\n",
"\n",
" # 保存结果\n",
" merged_df.to_csv(\"merged_with_smiles.csv\", index=False)\n",
" print(\"转换完成，已保存为 merged_with_smiles.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "032a4d8c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(11122, 10725)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(merged_df[\"CAS Number\"].unique()), len(merged_df['SMILES'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5e891d70",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(True, 2)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df['SMILES'].isna().any(), merged_df['SMILES'].isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "522c3c6b",
"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>Compound name</th>\n",
" <th>Catalog Number</th>\n",
" <th>CAS Number</th>\n",
" <th>MOA</th>\n",
" <th>Clinical Information</th>\n",
" <th>Approved Type</th>\n",
" <th>Catalog Number.1</th>\n",
" <th>PubChem_CID</th>\n",
" <th>SMILES</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5991</th>\n",
" <td>Atuveciclib S-Enantiomer</td>\n",
" <td>HY_12871C</td>\n",
" <td>250279-81-1</td>\n",
" <td>CDK</td>\n",
" <td>No Development Reported</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10734</th>\n",
" <td>FITC-Dextran (MW 10000)</td>\n",
" <td>HY_128868</td>\n",
" <td>60842-46-8</td>\n",
" <td>Others</td>\n",
" <td>No Development Reported</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Compound name Catalog Number CAS Number MOA \\\n",
"5991 Atuveciclib S-Enantiomer HY_12871C 250279-81-1 CDK \n",
"10734 FITC-Dextran (MW 10000) HY_128868 60842-46-8 Others \n",
"\n",
" Clinical Information Approved Type Catalog Number.1 PubChem_CID \\\n",
"5991 No Development Reported NaN NaN None \n",
"10734 No Development Reported NaN NaN None \n",
"\n",
" SMILES \n",
"5991 None \n",
"10734 None "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nan_rows = merged_df[merged_df['SMILES'].isna()]\n",
"nan_rows"
]
},
{
"cell_type": "markdown",
"id": "4380d330",
"metadata": {},
"source": [
"# 通过手动搜索来补全"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "977eb4bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Int64Index([5991], dtype='int64')\n",
"Int64Index([10734], dtype='int64')\n"
]
}
],
"source": [
"# 250279-81-1\t： N=[S@@](CC1=CC(NC2=NC(C3=CC=C(F)C=C3OC)=NC=N2)=CC=C1)(C)=O\n",
"# 60842-46-8： O=C1C2=CC=CC=C2C(=O)C1C3=CC=C(N=C=S)C=C3O 只能把“FITC 核心 + 连接臂”这部分拆开表示\n",
"\n",
"# 先确认行号\n",
"print(merged_df.loc[merged_df['CAS Number'] == '250279-81-1'].index) # 假设返回 5991\n",
"print(merged_df.loc[merged_df['CAS Number'] == '60842-46-8'].index) # 假设返回 10734\n",
"\n",
"# 一次性写进去\n",
"merged_df.loc[5991, 'SMILES'] = 'N=[S@@](CC1=CC(NC2=NC(C3=CC=C(F)C=C3OC)=NC=N2)=CC=C1)(C)=O'\n",
"merged_df.loc[10734, 'SMILES'] = 'O=C1C2=CC=CC=C2C(=O)C1C3=CC=C(N=C=S)C=C3O' # FITC 骨架"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e3bffa9d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(False, 0)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df['SMILES'].isna().any(), merged_df['SMILES'].isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d9f6a788",
"metadata": {},
"outputs": [],
"source": [
"merged_df.to_csv(\"CIGS_with_smiles.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "014e0723",
"metadata": {},
"outputs": [],
"source": [
"merged_df['SMILES'].to_csv('CIGS_smiles_list.csv', index=False, header=True)"
]
},
{
"cell_type": "markdown",
"id": "bcbfa041",
"metadata": {},
"source": [
"# 检查这些SMILES是否是规范化的\n",
"\n",
"## 上面的才是规范化的，用这个，下面这个如果原 SMILES 本身错/含非法字符，MolFromSmiles 直接返回 None，会丢行"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "70cc22cb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"规范化后的 SMILES 数组:\n",
"13221 13221\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[12:50:19] WARNING: not removing hydrogen atom without neighbors\n",
"[12:50:19] WARNING: not removing hydrogen atom without neighbors\n",
"[12:50:19] 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(merged_df['SMILES'])\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(\"规范化后的 SMILES 数组:\")\n",
"# print(len(smiles_array), len(canonical_smiles_array))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7014f8af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "ea42c2fb",
"metadata": {},
"source": [
"# 中药没有给CAS，所以漏了SMIELS，先用名字生成CAS，然后再生成SMIELS"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "dacc1145",
"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>Compound name</th>\n",
" <th>Catalog Number</th>\n",
" <th>CAS Number</th>\n",
" <th>MOA</th>\n",
" <th>Clinical Information</th>\n",
" <th>Approved Type</th>\n",
" <th>Catalog Number.1</th>\n",
" <th>PubChem_CID</th>\n",
" <th>SMILES</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11356</th>\n",
" <td>Acteoside</td>\n",
" <td>Cpd0001</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61276-17-3</td>\n",
" <td>445063</td>\n",
" <td>CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11357</th>\n",
" <td>Asiaticoside B</td>\n",
" <td>Cpd0002</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>125265-68-1</td>\n",
" <td>445063</td>\n",
" <td>CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11358</th>\n",
" <td>Brandioside</td>\n",
" <td>Cpd0003</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>133393-81-4</td>\n",
" <td>445063</td>\n",
" <td>CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11359</th>\n",
" <td>Clematichinenos\\nide AR</td>\n",
" <td>Cpd0004</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>761425-93-8</td>\n",
" <td>445063</td>\n",
" <td>CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11360</th>\n",
" <td>Saikosaponin\\nB1</td>\n",
" <td>Cpd0005</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>58558-08-0</td>\n",
" <td>445063</td>\n",
" <td>CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Compound name Catalog Number CAS Number MOA \\\n",
"11356 Acteoside Cpd0001 NaN NaN \n",
"11357 Asiaticoside B Cpd0002 NaN NaN \n",
"11358 Brandioside Cpd0003 NaN NaN \n",
"11359 Clematichinenos\\nide AR Cpd0004 NaN NaN \n",
"11360 Saikosaponin\\nB1 Cpd0005 NaN NaN \n",
"\n",
" Clinical Information Approved Type Catalog Number.1 PubChem_CID \\\n",
"11356 NaN NaN 61276-17-3 445063 \n",
"11357 NaN NaN 125265-68-1 445063 \n",
"11358 NaN NaN 133393-81-4 445063 \n",
"11359 NaN NaN 761425-93-8 445063 \n",
"11360 NaN NaN 58558-08-0 445063 \n",
"\n",
" SMILES \n",
"11356 CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O \n",
"11357 CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O \n",
"11358 CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O \n",
"11359 CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O \n",
"11360 CC(=O)NC1C(CC(OC1C(C(CO)O)O)(C(=O)O)O)O "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# merged_df.head()\n",
"\n",
"# 把merged_df中的Catalog Number列中以“Cpd”开头的行提取出来\n",
"merged_df_cpd = merged_df[merged_df['Catalog Number'].str.startswith('Cpd')]\n",
"merged_df_cpd.head()"
]
},
{
"cell_type": "markdown",
"id": "485f1864",
"metadata": {},
"source": [
"## name to SMILES"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26725140",
"metadata": {},
"outputs": [],
"source": [
"import re, requests, pandas as pd, time\n",
"\n",
"# 去掉换行、多余空格、括号备注\n",
"def clean_name(n):\n",
" return re.sub(r'\\s+', ' ', n.split('(')[0]).strip()\n",
"\n",
"name_list = [clean_name(n) for n in merged_df_cpd['Compound name'].tolist()]\n",
"name_list = [n for n in name_list if 2 <= len(n) <= 200] # 去掉极端长/短\n",
"\n",
"BATCH = 50\n",
"URL = \"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/property/CanonicalSMILES/TXT\"\n",
"\n",
"def names2smiles_post(names):\n",
" smi_map = {}\n",
" for i in range(0, len(names), BATCH):\n",
" chunk = names[i:i+BATCH]\n",
" payload = '\\n'.join(chunk) # POST 正文\n",
" r = requests.post(URL, data=payload, headers={'Content-Type': 'text/plain'}, timeout=60)\n",
" if r.status_code == 200:\n",
" for line in r.text.strip().split('\\n'):\n",
" if '\\t' in line:\n",
" n_query, smi = line.split('\\t')\n",
" if smi != 'N/A':\n",
" smi_map[n_query] = smi\n",
" else:\n",
" print(f\"批次 {i//BATCH + 1} 失败：{r.status_code}\")\n",
" time.sleep(0.5)\n",
" return smi_map\n",
"\n",
"smiles_dict = names2smiles_post(name_list)\n",
"print('命中率：', len(smiles_dict)/len(name_list))"
]
},
{
"cell_type": "markdown",
"id": "43405512",
"metadata": {},
"source": [
"# 失败，放弃中药"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1dcdb41",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c3bf93e0",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import numpy as np\n",
"import pyarrow as pa\n",
"import pyarrow.parquet as pq\n",
"\n",
"file_path = \"/data/boom/CIGS/MDA-MB-231-JQ1-RNA-seq-RawCounts.xlsx\"\n",
"\n",
"meta = pd.read_excel(file_path, sheet_name=0, engine='openpyxl',\n",
" header=1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7e226bfa",
"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>Ensembl</th>\n",
" <th>DMSO_1</th>\n",
" <th>DMSO_2</th>\n",
" <th>DMSO_3</th>\n",
" <th>JQ1_1</th>\n",
" <th>JQ1_2</th>\n",
" <th>JQ1_3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ENSG00000000003.15</td>\n",
" <td>335</td>\n",
" <td>334</td>\n",
" <td>428</td>\n",
" <td>285</td>\n",
" <td>261</td>\n",
" <td>330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ENSG00000000005.6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ENSG00000000419.13</td>\n",
" <td>1624</td>\n",
" <td>1615</td>\n",
" <td>1781</td>\n",
" <td>1512</td>\n",
" <td>1471</td>\n",
" <td>1763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ENSG00000000457.14</td>\n",
" <td>69</td>\n",
" <td>100</td>\n",
" <td>126</td>\n",
" <td>90</td>\n",
" <td>107</td>\n",
" <td>152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ENSG00000000460.17</td>\n",
" <td>314</td>\n",
" <td>301</td>\n",
" <td>338</td>\n",
" <td>74</td>\n",
" <td>63</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60646</th>\n",
" <td>ENSG00000288695.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60647</th>\n",
" <td>ENSG00000288696.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60648</th>\n",
" <td>ENSG00000288697.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60649</th>\n",
" <td>ENSG00000288698.1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60650</th>\n",
" <td>ENSG00000288699.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>60651 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Ensembl DMSO_1 DMSO_2 DMSO_3 JQ1_1 JQ1_2 JQ1_3\n",
"0 ENSG00000000003.15 335 334 428 285 261 330\n",
"1 ENSG00000000005.6 0 0 0 0 0 0\n",
"2 ENSG00000000419.13 1624 1615 1781 1512 1471 1763\n",
"3 ENSG00000000457.14 69 100 126 90 107 152\n",
"4 ENSG00000000460.17 314 301 338 74 63 89\n",
"... ... ... ... ... ... ... ...\n",
"60646 ENSG00000288695.1 0 0 0 0 0 0\n",
"60647 ENSG00000288696.1 0 0 0 0 0 0\n",
"60648 ENSG00000288697.1 0 0 0 0 0 0\n",
"60649 ENSG00000288698.1 0 1 1 0 0 0\n",
"60650 ENSG00000288699.1 0 0 0 0 0 0\n",
"\n",
"[60651 rows x 7 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meta"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b4d7a01",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "boom",
"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": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|