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{
"cells": [
{
"cell_type": "markdown",
"id": "2e29f3a3-381c-4c16-853c-d73d38abb383",
"metadata": {
"libroFormatter": "formatter-string",
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"source": [
"# 1. 加载数据，查看数据格式\n",
"# 2. 使用 data_transform.py 将 Uniprot_id 格式转为 Saprot 可以接受的 Foldseek Seq 格式\n",
"# 3. 记录所有 Target_Uniprot_id 和 Compound_Smiles 及其 对应信息"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bd7b0b18-c1f1-4f1c-be46-a3bd4686ca57",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
"assay_id P00316\n",
"target_id ROS1\n",
"compound_id EB000590\n",
"mode Binding\n",
"mechanism Competition Binding\n",
"outcome_is_active True\n",
"outcome_potency_pxc50 11.8\n",
"outcome_max_activity 99.3\n",
"observed_max 100.0\n",
"is_quantified True\n",
"frequency_flag False\n",
"viability_flag False\n",
"pxc50_modifier >\n",
"slope 0.6\n",
"asymp_min 58.0\n",
"asymp_max 99.3\n",
"assay__technology TR-FRET\n",
"target__class Kinase\n",
"target__gene ROS1\n",
"target__uniprot_id P08922\n",
"target__is_mutant False\n",
"target__wildtype_id ROS1\n",
"target__name Proto-oncogene tyrosine-protein kinase ROS\n",
"compound__name Lorlatinib\n",
"compound__smiles C[C@H]1OC2=C(N)N=CC(=C2)C2=C(C#N)N(C)N=C2CN(C)...\n",
"compound__drugbank_id DB12130\n",
"compound__cas 1454846-35-5\n",
"compound__unii OSP71S83EU\n",
"compound__inchikey IIXWYSCJSQVBQM-LLVKDONJSA-N\n",
"progressed True\n",
"release 8\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# 读取 parquet 文件\n",
"data_path = 'drug_target_activity/train.parquet'\n",
"df = pd.read_parquet(data_path)\n",
"\n",
"# 查看一个example\n",
"df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "21412976-18ab-44d4-b73d-18bb0a883f0f",
"metadata": {
"execution": {
"shell.execute_reply.end": "2025-12-26T07:18:30.046621Z",
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},
"isLargeOutputDisplay": true,
"libroFormatter": "formatter-string",
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"正在加载数据集配置...\n",
"------------------------------\n",
"总数据量: 421894\n",
"野生型 (False): 406527 条 (96.36%)\n",
"突变体 (True) : 15367 条 (3.64%)\n",
"------------------------------\n"
]
}
],
"source": [
"def check_mutant_ratio(df):\n",
" print(\"正在加载数据集配置...\")\n",
" # 加载数据集 (假设你已经登录或数据集是公开的)\n",
" # 如果只是为了统计，可以使用 streaming=True 来避免下载整个数据集，但全量统计需要遍历\n",
" # 这里假设显存/内存足够，直接加载 train split\n",
" try:\n",
" # ds = load_dataset(\"eve-bio/drug-target-activity\", split=\"train\")\n",
" \n",
" # 将其转换为 pandas DataFrame 以便处理\n",
" # 为了节省内存，只取 target__is_mutant 这一列\n",
" # print(\"正在转换数据...\")\n",
" # df = ds.select_columns([\"target__is_mutant\"]).to_pandas()\n",
" \n",
" # 统计数量\n",
" counts = df['target__is_mutant'].value_counts()\n",
" total = len(df)\n",
" \n",
" # 计算比例\n",
" false_ratio = (counts.get(False, 0) / total) * 100\n",
" true_ratio = (counts.get(True, 0) / total) * 100\n",
" \n",
" print(\"-\" * 30)\n",
" print(f\"总数据量: {total}\")\n",
" print(f\"野生型 (False): {counts.get(False, 0)} 条 ({false_ratio:.2f}%)\")\n",
" print(f\"突变体 (True) : {counts.get(True, 0)} 条 ({true_ratio:.2f}%)\")\n",
" print(\"-\" * 30)\n",
" except Exception as e:\n",
" print(f\"发生错误: {e}\")\n",
"check_mutant_ratio(df)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1b7faa51-db68-4a52-823f-a4a27d44c142",
"metadata": {
"execution": {
"shell.execute_reply.end": "2025-12-26T07:18:35.112954Z",
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"to_execute": "2025-12-26T07:18:35.222Z"
},
"isLargeOutputDisplay": true,
"libroFormatter": "formatter-string",
"trusted": true
},
"outputs": [],
"source": [
"# 建立 uniprot_id -> foldseek seq 的 map 并 save\n",
"# from dataset_transform import generate_and_save_foldseek_dict\n",
"# uniprot_ids = get_unique_uniprot_ids(data_path)\n",
"# 这里在另外一个电脑上做的数据爬取，所以两个path没有具体写定\n",
"map_save_path = 'drug_target_activity/protein_foldseek_seqs.json'\n",
"foldseek_path = 'path/to/foldseek'\n",
"# generate_and_save_foldseek_dict(uniprot_ids, map_save_path, foldseek_path)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "51ddcbbb-0e0a-4fec-8c67-698b14ad8e34",
"metadata": {
"isLargeOutputDisplay": true,
"libroFormatter": "formatter-string",
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 1: 原始数据加载完成，当前数据量: 421894\n",
"Step 2: 筛选非突变体 (is_mutant=False) 后，当前数据量: 406527\n",
"Step 3: Map文件加载完成，包含 138 个 ID 映射\n",
"Step 4: 筛选 Uniprot ID 存在于 Map 中的数据后，当前数据量: 318516\n",
"Step 5: 处理完成，最终文件已保存至: drug_target_activity/processed_train.parquet\n"
]
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"def build_dataset(data_path, foldseek_map_path, new_dataset_path):\n",
" '''\n",
" 1. 打开data_path的parquet文件\n",
" 2. 打开foldseek_map_path的json文件, 读取 dict, 其中 key:value 为 uniprot_id:foldseek seq\n",
" 3. 筛选'target__is_mutant'为false的 row\n",
" 4. 筛选dataset中'target__uniprot_id' 在 dict 的 key 中的 row, 并增加一列'target__foldseek_seq', 值为 dict 中对应的 value\n",
" 5. 保存newdataset到new_dataset_path\n",
" '''\n",
" df = pd.read_parquet(data_path)\n",
" print(f\"Step 1: 原始数据加载完成，当前数据量: {len(df)}\")\n",
"\n",
" df = df[df['target__is_mutant'] == False]\n",
" print(f\"Step 2: 筛选非突变体 (is_mutant=False) 后，当前数据量: {len(df)}\")\n",
"\n",
" with open(foldseek_map_path, 'r') as f:\n",
" foldseek_map = json.load(f)\n",
" print(f\"Step 3: Map文件加载完成，包含 {len(foldseek_map)} 个 ID 映射\")\n",
"\n",
" df = df[df['target__uniprot_id'].isin(foldseek_map.keys())].copy()\n",
" print(f\"Step 4: 筛选 Uniprot ID 存在于 Map 中的数据后，当前数据量: {len(df)}\")\n",
"\n",
" df['target__foldseek_seq'] = df['target__uniprot_id'].map(foldseek_map)\n",
"\n",
" try:\n",
" df.to_parquet(new_dataset_path)\n",
" print(f\"Step 5: 处理完成，最终文件已保存至: {new_dataset_path}\")\n",
" except Exception as e:\n",
" print(f\"保存文件失败: {e}\")\n",
"\n",
"# 示例调用（如果需要测试）\n",
"new_dataset_path = 'drug_target_activity/processed_train.parquet'\n",
"build_dataset(data_path, map_save_path, new_dataset_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6a28872-f377-47ed-b3e8-903c8e25567e",
"metadata": {
"isLargeOutputDisplay": true,
"libroFormatter": "formatter-string",
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"创建目录: drug_target_activity/candidates\n",
"正在读取文件: drug_target_activity/processed_train.parquet ...\n",
"正在提取 Unique Target 信息...\n",
"Target 信息已保存至: drug_target_activity/candidates/unique_targets.json (数量: 138)\n",
"正在提取 Unique Compound 信息...\n",
"Compound 信息已保存至: drug_target_activity/candidates/unique_compounds.json (数量: 1382)\n"
]
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"import os\n",
"\n",
"import pandas as pd\n",
"import json\n",
"import os\n",
"\n",
"def extract_unique_entities(parquet_path, output_dir):\n",
" \"\"\"\n",
" 读取 Parquet 文件，提取唯一的 Protein (含 foldseek seq) 和 Molecule 信息，并保存为 JSON。\n",
" \"\"\"\n",
" \n",
" # 1. 确保输出目录存在\n",
" if not os.path.exists(output_dir):\n",
" os.makedirs(output_dir)\n",
" print(f\"创建目录: {output_dir}\")\n",
"\n",
" print(f\"正在读取文件: {parquet_path} ...\")\n",
" try:\n",
" df = pd.read_parquet(parquet_path)\n",
" except Exception as e:\n",
" print(f\"读取 Parquet 失败: {e}\")\n",
" return\n",
"\n",
" # ==========================================\n",
" # 2. 处理 Proteins (Targets)\n",
" # ==========================================\n",
" print(\"正在提取 Unique Target 信息...\")\n",
" \n",
" # 【修改点】加入了 'target__foldseek_seq'\n",
" target_cols = ['target__uniprot_id', 'target__foldseek_seq', 'target__class', 'target__gene']\n",
" \n",
" # 检查列是否存在\n",
" existing_target_cols = [c for c in target_cols if c in df.columns]\n",
" \n",
" if 'target__uniprot_id' in existing_target_cols and 'target__foldseek_seq' in existing_target_cols:\n",
" # 提取列 -> 去除 ID 为空的行 -> 根据 ID 去重 \n",
" # 注意：这里假设同一个 ID 对应的 seq 是一样的，只保留第一条\n",
" target_df = df[existing_target_cols].dropna(subset=['target__uniprot_id'])\n",
" target_df = target_df.drop_duplicates(subset=['target__uniprot_id'])\n",
" \n",
" # 将 NaN 替换为 None\n",
" target_df = target_df.where(pd.notnull(target_df), None)\n",
" \n",
" # 转换为字典: \n",
" # { \n",
" # \"UniprotID\": { \n",
" # \"target__foldseek_seq\": \"...\", \n",
" # \"target__class\": \"...\", \n",
" # ... \n",
" # } \n",
" # }\n",
" target_data = target_df.set_index('target__uniprot_id').to_dict(orient='index')\n",
" \n",
" target_out_path = os.path.join(output_dir, 'unique_targets.json')\n",
" with open(target_out_path, 'w', encoding='utf-8') as f:\n",
" json.dump(target_data, f, indent=4, ensure_ascii=False)\n",
" print(f\"Target 信息已保存至: {target_out_path} (数量: {len(target_data)})\")\n",
" else:\n",
" print(\"警告: 数据中缺少 'target__uniprot_id' 或 'target__foldseek_seq' 列，跳过 Target 提取。\")\n",
"\n",
" # ==========================================\n",
" # 3. 处理 Molecules (Compounds)\n",
" # ==========================================\n",
" print(\"正在提取 Unique Compound 信息...\")\n",
" \n",
" compound_cols = [\n",
" 'compound__smiles', \n",
" 'compound__name', \n",
" 'compound__drugbank_id', \n",
" 'compound__cas', \n",
" 'compound__unii', \n",
" 'compound__inchikey'\n",
" ]\n",
" \n",
" existing_compound_cols = [c for c in compound_cols if c in df.columns]\n",
" \n",
" if 'compound__smiles' in existing_compound_cols:\n",
" # 提取列 -> 去除 SMILES 为空的行 -> 根据 SMILES 去重\n",
" mol_df = df[existing_compound_cols].dropna(subset=['compound__smiles'])\n",
" mol_df = mol_df.drop_duplicates(subset=['compound__smiles'])\n",
" \n",
" # 将 NaN 替换为 None\n",
" mol_df = mol_df.where(pd.notnull(mol_df), None)\n",
" \n",
" # 转换为字典: { \"SMILES\": { \"compound__name\": \"...\", ... } }\n",
" mol_data = mol_df.set_index('compound__smiles').to_dict(orient='index')\n",
" \n",
" mol_out_path = os.path.join(output_dir, 'unique_compounds.json')\n",
" with open(mol_out_path, 'w', encoding='utf-8') as f:\n",
" json.dump(mol_data, f, indent=4, ensure_ascii=False)\n",
" print(f\"Compound 信息已保存至: {mol_out_path} (数量: {len(mol_data)})\")\n",
" else:\n",
" print(\"警告: 数据中缺少 'compound__smiles' 列，跳过 Compound 提取。\")\n",
"\n",
"# --- 使用示例 ---\n",
"dataset_path = 'drug_target_activity/processed_train.parquet'\n",
"output_directory = 'drug_target_activity/candidates'\n",
"extract_unique_entities(dataset_path, output_directory)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee0d150f",
"metadata": {},
"outputs": [],
"source": []
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