{"id":38583,"date":"2025-02-18T23:00:53","date_gmt":"2025-02-18T15:00:53","guid":{"rendered":"https:\/\/17aitech.com\/?p=38583"},"modified":"2025-02-18T23:00:53","modified_gmt":"2025-02-18T15:00:53","slug":"tomg-bench%ef%bc%9a%e5%a4%a7%e8%af%ad%e8%a8%80%e6%a8%a1%e5%9e%8b%e5%bc%80%e6%94%be%e5%9f%9f%e5%88%86%e5%ad%90%e7%94%9f%e6%88%90%e6%96%b0%e5%9f%ba%e5%87%86","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=38583","title":{"rendered":"TOMG-Bench\uff1a\u5927\u8bed\u8a00\u6a21\u578b\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u65b0\u57fa\u51c6"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:TOMG-Bench\uff1a\u5927\u8bed\u8a00\u6a21\u578b\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u65b0\u57fa\u51c6<\/a><\/p>\n

<\/a><\/p>\n

\u7f16\u8f91 |\u00a0ScienceAI<\/p>\n

\u79d1\u5b66\u5bb6\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u7684\u57fa\u51c6<\/mark>\u6d4b\u8bd5\u2014\u2014TOMG-Bench\uff0c\u7528\u4e8e\u8bc4\u4f30 LLM \u5728\u5206\u5b50\u9886\u57df\u7684\u5f00\u653e\u57df\u751f\u6210\u80fd\u529b\u3002<\/p>\n

<\/a><\/p>\n

\u9879\u76ee\u4e3b\u9875\uff1ahttps:\/\/phenixace.github.io\/tomgbench\/<\/em><\/p>\n

\u6570\u636e\u96c6\u548c\u6d4b\u8bd5\u811a\u672c\uff1ahttps:\/\/github.com\/phenixace\/TOMG-Bench<\/em><\/p>\n

\u9884\u5370\u672c\uff1ahttps:\/\/arxiv.org\/abs\/2412.14642<\/em><\/p>\n

Huggingface Datasets\uff1ahttps:\/\/huggingface.co\/datasets\/Duke-de-Artois\/TOMG-Bench<\/em><\/p>\n

PaperWithCode\uff1ahttps:\/\/paperswithcode.com\/dataset\/tomg-bench<\/em><\/p>\n

\u5206\u5b50\u53d1\u73b0\u662f\u63a8\u52a8\u533b\u836f\u3001\u6750\u6599\u79d1\u5b66\u7b49\u9886\u57df\u8fdb\u6b65\u7684\u5173\u952e\u73af\u8282\u3002\u7136\u800c\uff0c\u4f20\u7edf\u7684\u5206\u5b50\u53d1\u73b0\u65b9\u6cd5\u5f80\u5f80\u4f9d\u8d56\u4e8e\u53cd\u590d\u5b9e\u9a8c\u548c\u6570\u636e\u5206\u6790<\/mark>\uff0c\u6548\u7387\u4f4e\u4e0b\u4e14\u6210\u672c\u9ad8\u6602\u3002<\/p>\n

\u968f\u7740\u673a\u5668\u5b66\u4e60<\/mark>\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u56fe\u795e\u7ecf\u7f51\u7edc<\/mark>\uff08GNN\uff09\u7b49 AI \u5de5\u5177\u5728\u5206\u5b50\u53d1\u73b0\u9886\u57df\u5c55\u73b0\u51fa\u5de8\u5927\u7684\u6f5c\u529b\u3002\u7136\u800c\uff0cGNN \u65b9\u6cd5\u4e5f\u5b58\u5728\u5c40\u9650\u6027\uff0c\u4f8b\u5982\u96be\u4ee5\u6cdb\u5316\u5230\u4e0d\u540c\u4efb\u52a1\uff0c\u4ee5\u53ca\u65e0\u6cd5\u751f\u6210\u5177\u6709\u7279\u5b9a\u6027\u8d28\u7684\u5206\u5b50\u7ed3\u6784\u3002<\/p>\n

\u5927\u8bed\u8a00\u6a21\u578b<\/mark>\uff08LLM\uff09\u51ed\u501f\u5176\u5f3a\u5927\u7684\u8bed\u8a00\u7406\u89e3\u548c\u751f\u6210\u80fd\u529b\u4ee5\u53ca\u6cdb\u5316\u80fd\u529b\uff0c\u4e3a\u5206\u5b50\u53d1\u73b0\u9886\u57df\u5e26\u6765\u4e86\u65b0\u7684\u673a\u9047\u3002LLM \u53ef\u4ee5\u5c06\u5206\u5b50\u7ed3\u6784\u4ee5\u6587\u672c\u5f62\u5f0f\u8fdb\u884c\u8868\u793a\uff0c\u4ece\u800c\u7406\u89e3\u5206\u5b50\u7684\u7ed3\u6784\u548c\u6027\u8d28\uff0c\u5e76\u53ef\u4ee5\u751f\u6210\u65b0\u7684\u5206\u5b50\u7ed3\u6784\u3002<\/p>\n

\u7136\u800c\uff0c\u5c06\u5206\u5b50\u4e0e\u6587\u672c\u6570\u636e\u8fdb\u884c\u5bf9\u9f50\u662f\u4e00\u4e2a\u5177\u6709\u6311\u6218\u6027\u7684\u95ee\u9898\u3002\u5148\u524d\u7684\u5206\u5b50-\u63cf\u8ff0\u6587\u672c\u6570\u636e\u96c6\u5982 ChEBI-20 \u548c\u00a0PubChem\u00a0\u90fd\u662f\u57fa\u4e8e\u7ed9\u5b9a\u7684\u63cf\u8ff0\u751f\u6210\u552f\u4e00\u5bf9\u5e94\u7684\u5206\u5b50\uff08\u57fa\u4e8e\u6587\u672c\u7684\u76ee\u6807\u5bfc\u5411\u7684\u5206\u5b50\u751f\u6210\uff09\uff0c\u800c\u73b0\u5b9e\u4e2d\uff0c\u5316\u5b66\u5bb6\u8bbe\u8ba1\u5206\u5b50\u7684\u9700\u6c42\u5f80\u5f80\u662f\u6a21\u7cca\u7684\uff0c\u53ef\u4ee5\u5bf9\u5e94\u5230\u591a\u4e2a\u7b26\u5408\u8981\u6c42\u7684\u5206\u5b50\uff0c\u5982\u56fe 1 \u6240\u793a\u3002\u56e0\u6b64\uff0c\u9700\u8981\u5f00\u53d1\u65b0\u7684\u6570\u636e\u96c6\u548c\u57fa\u51c6<\/mark>\u6d4b\u8bd5\u6765\u8bc4\u4f30 LLM \u5728\u5206\u5b50\u53d1\u73b0\u4e2d\u7684\u6027\u80fd\u3002<\/p>\n

\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u6311\u6218\uff0c\u9999\u6e2f\u7406\u5de5\u5927\u5b66\u3001\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u3001\u4e0a\u6d77\u4eba\u5de5\u667a\u80fd\u5b9e\u9a8c\u7684\u7814\u7a76\u8005\u63d0\u51fa\u4e86\u57fa\u4e8e\u6587\u672c\u7684\u5f00\u653e\u5206\u5b50\u751f\u6210\u57fa\u51c6<\/mark>\u6d4b\u8bd5\uff08TOMG-Bench\uff09\uff0c\u65e8\u5728\u8bc4\u4f30 LLM \u5728\u5206\u5b50\u9886\u57df\u7684\u5f00\u653e\u57df\u751f\u6210\u80fd\u529b\u3002<\/p>\n

TOMG-Bench \u5305\u542b\u4e09\u4e2a\u4e3b\u8981\u4efb\u52a1\uff1a\u5206\u5b50\u7f16\u8f91\u3001\u5206\u5b50\u4f18\u5316\u548c\u5b9a\u5236\u5206\u5b50\u751f\u6210\uff0c\u6db5\u76d6\u4e86\u5206\u5b50\u53d1\u73b0\u7684\u591a\u4e2a\u5173\u952e\u73af\u8282\u3002\u901a\u8fc7 TOMG-Bench\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u4e86\u89e3 LLM \u5728\u5206\u5b50\u53d1\u73b0\u4e2d\u7684\u4f18\u52bf\u548c\u5c40\u9650\u6027\uff0c\u5e76\u63a8\u52a8 LLM \u5728\u5206\u5b50\u53d1\u73b0\u9886\u57df\u7684\u5e94\u7528\u3002<\/p>\n

<\/a><\/p>\n

\u56fe 1\uff1a\u5bf9\u6bd4\u57fa\u4e8e\u6587\u672c\u7684\u76ee\u6807\u5bfc\u5411\u7684\u5206\u5b50\u751f\u6210 (a) \u548c\u5f00\u653e\u5f0f\u5206\u5b50\u751f\u6210 (b)<\/p>\n

\u73b0\u6709\u5206\u5b50-\u6587\u672c\u5bf9\u9f50\u9762\u4e34\u7684\u6311\u6218<\/strong><\/p>\n

1. \u6570\u636e\u96c6\u548c\u57fa\u51c6<\/mark>\u6d4b\u8bd5\u7684\u4e0d\u8db3\uff1a<\/strong>\u73b0\u6709\u7684\u6570\u636e\u96c6\uff0c\u4f8b\u5982 ChEBI-20 \u548c PubChem\uff0c\u90fd\u4f9d\u8d56\u4e8e\u5206\u5b50-\u63cf\u8ff0\u5bf9\uff0c\u8fd9\u4e9b\u6570\u636e\u96c6\u65e0\u6cd5\u6ee1\u8db3\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u4efb\u52a1\u7684\u9700\u6c42\u3002\u8fd9\u4e9b\u6570\u636e\u96c6\u5f80\u5f80\u662f\u57fa\u4e8e\u7ed9\u5b9a\u7684\u63cf\u8ff0\u751f\u6210\u552f\u4e00\u5bf9\u5e94\u7684\u5206\u5b50\uff0c\u800c\u73b0\u5b9e\u4e2d\uff0c\u5316\u5b66\u5bb6\u8bbe\u8ba1\u5206\u5b50\u7684\u9700\u6c42\u5f80\u5f80\u662f\u6a21\u7cca\u7684\uff0c\u53ef\u4ee5\u5bf9\u5e94\u5230\u591a\u4e2a\u7b26\u5408\u8981\u6c42\u7684\u5206\u5b50\u3002<\/p>\n

2. \u5206\u5b50-\u63cf\u8ff0\u7ffb\u8bd1\u4efb\u52a1\u7684\u5c40\u9650\u6027\uff1a<\/strong>\u5206\u5b50-\u63cf\u8ff0\u7ffb\u8bd1\u4efb\u52a1\u662f\u8de8\u8d8a\u5206\u5b50\u7a7a\u95f4\u548c\u81ea\u7136\u8bed\u8a00\u7a7a\u95f4\u7684\u6865\u6881\uff0c\u4f46\u5b58\u5728\u5c40\u9650\u6027\u3002\u4e00\u65b9\u9762\uff0c\u771f\u5b9e\u573a\u666f\u4e2d\u7684\u5206\u5b50\u63cf\u8ff0\u53ef\u80fd\u9ad8\u5ea6\u6a21\u7cca\uff0c\u5b58\u5728\u591a\u79cd\u6b63\u786e\u89e3\u91ca\uff0c\u800c\u73b0\u6709\u7684\u5206\u5b50-\u63cf\u8ff0\u7ffb\u8bd1\u5b9e\u9645\u4e0a\u662f\u76ee\u6807\u751f\u6210\u4efb\u52a1\uff0c\u6a21\u578b\u96be\u4ee5\u6cdb\u5316\u5230\u7528\u6237\u5b9a\u5236\u7684\u8981\u6c42\u3002<\/p>\n

3. \u65e0\u6cd5\u751f\u6210\u65b0\u5206\u5b50\u7ed3\u6784\uff1a<\/strong>\u73b0\u6709\u7684\u5206\u5b50-\u63cf\u8ff0\u7ffb\u8bd1\u4efb\u52a1\u548c\u76f8\u5e94\u7684\u8bc4\u4f30\u6307\u6807\u65e0\u6cd5\u8bc4\u4f30 LLM \u751f\u6210\u65b0\u5206\u5b50\u7ed3\u6784\u7684\u80fd\u529b\uff0c\u800c\u8fd9\u662f\u5206\u5b50\u53d1\u73b0\u7684\u6700\u7ec8\u76ee\u6807\uff0c\u5c24\u5176\u5728\u836f\u7269\u53d1\u73b0<\/mark>\u9886\u57df\u3002<\/p>\n

TOMG-Bench<\/strong><\/p>\n

TOMG-Bench \u662f\u4e00\u4e2a\u521b\u65b0\u7684\u57fa\u51c6<\/mark>\u6d4b\u8bd5\uff0c\u65e8\u5728\u5168\u9762\u8bc4\u4f30 LLM \u5728\u5206\u5b50\u9886\u57df\u7684\u5f00\u653e\u57df\u751f\u6210\u80fd\u529b\u3002\u4e0e\u4f20\u7edf\u7684\u57fa\u4e8e\u6587\u672c\u7684\u76ee\u6807\u5bfc\u5411\u5206\u5b50\u751f\u6210\u4efb\u52a1\u4e0d\u540c\uff0cTOMG-Bench \u7684\u4efb\u52a1\u662f\u5f00\u653e\u57df\u7684\uff0c\u5373\u4e0d\u8bbe\u5b9a\u7279\u5b9a\u7684\u76ee\u6807\u5206\u5b50\uff0c\u800c\u662f\u8ba9 LLM \u751f\u6210\u6ee1\u8db3\u7279\u5b9a\u8981\u6c42\u7684\u5206\u5b50\u7ed3\u6784\u3002\u8fd9\u79cd\u5f00\u653e\u6027\u66f4\u63a5\u8fd1\u5316\u5b66\u5bb6\u5728\u5b9e\u9645\u5de5\u4f5c\u4e2d\u9047\u5230\u7684\u9700\u6c42\uff0c\u66f4\u80fd\u4f53\u73b0 LLM \u7684\u6cdb\u5316\u80fd\u529b\u548c\u521b\u9020\u529b\u3002\u5b83\u5305\u542b\u4e09\u4e2a\u4e3b\u8981\u4efb\u52a1\uff0c\u6bcf\u4e2a\u4efb\u52a1\u53c8\u7ec6\u5206\u4e3a\u4e09\u4e2a\u5b50\u4efb\u52a1\uff0c\u6db5\u76d6\u5206\u5b50\u53d1\u73b0\u7684\u591a\u4e2a\u5173\u952e\u73af\u8282\uff1a<\/p>\n

1. \u5206\u5b50\u7f16\u8f91\uff08MolEdit\uff09<\/p>\n

\u6dfb\u52a0\u7ec4\u4ef6 (AddComponent): \u6307\u4ee4 LLM \u5411\u7ed9\u5b9a\u5206\u5b50\u6dfb\u52a0\u7279\u5b9a\u5b98\u80fd\u56e2\u3002<\/p>\n

\u5220\u9664\u7ec4\u4ef6 (DelComponent): \u6307\u4ee4 LLM \u4ece\u7ed9\u5b9a\u5206\u5b50\u4e2d\u5220\u9664\u7279\u5b9a\u5b98\u80fd\u56e2\u3002<\/p>\n

\u66ff\u6362\u7ec4\u4ef6 (SubComponent): \u6307\u4ee4 LLM \u4ece\u7ed9\u5b9a\u5206\u5b50\u4e2d\u5220\u9664\u7279\u5b9a\u5b98\u80fd\u56e2\uff0c\u5e76\u6dfb\u52a0\u65b0\u5b98\u80fd\u56e2\u3002<\/p>\n

2. \u5206\u5b50\u4f18\u5316\uff08MolOpt\uff09<\/p>\n

\u4f18\u5316 LogP \u503c (LogP): \u6307\u4ee4 LLM \u4f18\u5316\u5206\u5b50\u7ed3\u6784\uff0c\u4f7f\u5176 LogP \u503c\u964d\u4f4e\u6216\u5347\u9ad8\u3002<\/p>\n

\u4f18\u5316 MR \u503c (MR): \u6307\u4ee4 LLM \u4f18\u5316\u5206\u5b50\u7ed3\u6784\uff0c\u4f7f\u5176 MR \u503c\u964d\u4f4e\u6216\u5347\u9ad8\u3002<\/p>\n

\u4f18\u5316 QED \u503c (QED): \u6307\u4ee4 LLM \u4f18\u5316\u5206\u5b50\u7ed3\u6784\uff0c\u4f7f\u5176 QED \u503c\u964d\u4f4e\u6216\u5347\u9ad8\u3002<\/p>\n

3. \u5b9a\u5236\u5206\u5b50\u751f\u6210\uff08MolCustom\uff09<\/p>\n

\u6307\u5b9a\u539f\u5b50\u6570\u91cf\uff08AtomNum\uff09: \u6307\u4ee4 LLM \u751f\u6210\u6307\u5b9a\u6570\u91cf\u548c\u7c7b\u578b\u7684\u539f\u5b50\u7ec4\u6210\u7684\u5206\u5b50\u3002<\/p>\n

\u6307\u5b9a\u952e\u6570\u91cf\uff08BondNum\uff09: \u6307\u4ee4 LLM \u751f\u6210\u6307\u5b9a\u6570\u91cf\u548c\u7c7b\u578b\u7684\u952e\u7ec4\u6210\u7684\u5206\u5b50\u3002<\/p>\n

\u6307\u5b9a\u5b98\u80fd\u56e2\uff08FunctionalGroup\uff09: \u6307\u4ee4 LLM \u751f\u6210\u5305\u542b\u7279\u5b9a\u5b98\u80fd\u56e2\u7684\u5206\u5b50\u3002<\/p>\n

\u6bcf\u4e2a\u5b50\u4efb\u52a1\u90fd\u5305\u542b 5000 \u4e2a\u6d4b\u8bd5\u6837\u672c\uff0c\u4e3a LLM \u5728\u5206\u5b50\u9886\u57df\u7684\u5f00\u653e\u57df\u751f\u6210\u80fd\u529b\u63d0\u4f9b\u4e86\u5168\u9762\u7684\u8bc4\u4f30\u3002\u56fe 2 \u5c55\u793a\u4e86\u8fd9\u4e9b\u4efb\u52a1\u7684\u793a\u4f8b\u3002<\/p>\n

<\/a><\/p>\n

\u56fe 2: TOMG-Bench \u4e2d\u4efb\u52a1\u7684\u793a\u4f8b\u3002\u5176\u4e2d\u53f3\u4fa7\u7684\u5206\u5b50\u90fd\u88ab\u8ba4\u4e3a\u662f\u6b63\u786e\u7684\u3002<\/p>\n

\u6570\u636e\u751f\u6210<\/strong><\/p>\n

TOMG-Bench \u7684\u6d4b\u8bd5\u7528\u4f8b\u4e3b\u8981\u9488\u5bf9 MolEdit \u548c MolOpt \u4e24\u4e2a\u4efb\u52a1\u7c7b\u578b\u751f\u6210\uff0cMolCustom \u4efb\u52a1\u7c7b\u578b\u7684\u6d4b\u8bd5\u7528\u4f8b\u5219\u6839\u636e\u9700\u6c42\u968f\u673a\u751f\u6210\u3002<\/p>\n

1. MolEdit \u548c MolOpt \u4efb\u52a1\uff1a<\/p>\n

\u6570\u636e\u6765\u6e90\uff1a\u4ece Zinc250K \u6570\u636e\u5e93<\/mark>\u4e2d\u968f\u673a\u62bd\u53d6\u5206\u5b50\u4f5c\u4e3a\u6d4b\u8bd5\u6837\u672c\u3002<\/p>\n

\u5206\u5b50\u7edf\u8ba1\uff1a\u4f7f\u7528 RDKit \u5de5\u5177\u7bb1\u6536\u96c6\u5206\u5b50\u7684\u57fa\u672c\u7edf\u8ba1\u6570\u636e\uff0c\u5305\u62ec\u5206\u5b50\u7ed3\u6784\u6a21\u5f0f\u3001\u5316\u5b66\u6027\u8d28\uff08\u5982 LogP\u3001MR\u3001QED \u503c\uff09\u7b49\u3002<\/p>\n

\u4efb\u52a1 Prompt\uff1a\u5c06\u6536\u96c6\u5230\u7684\u5206\u5b50\u7edf\u8ba1\u6570\u636e\u6574\u5408\u5230\u9884\u5148\u5b9a\u4e49\u7684\u4efb\u52a1\u63d0\u793a\u4e2d\uff0c\u4f8b\u5982\uff1a<\/p>\n

MolEdit \u4efb\u52a1\uff1a\u5177\u4f53\u5982\u4e0b\u8868\u6240\u793a\uff0c\u4f8b\u5982\uff1a\u300c\u8bf7\u5c06\u5206\u5b50 c1ccccc1O \u4e2d\u7684\u7f9f\u57fa\u66ff\u6362\u4e3a\u7fa7\u57fa\u3002\u300d<\/p>\n

<\/a><\/p>\n

MolOpt \u4efb\u52a1\uff1a\u5177\u4f53\u5982\u4e0b\u8868\u6240\u793a\uff0c\u4f8b\u5982\uff1a\u300c\u8bf7\u4f18\u5316\u5206\u5b50 c1ccccc1O\uff0c\u4f7f\u5176 LogP \u503c\u964d\u4f4e\u3002\u300d<\/p>\n

<\/a><\/p>\n

2. MolCustom \u4efb\u52a1\uff1a<\/p>\n

\u6570\u636e\u6765\u6e90\uff1a\u968f\u673a\u751f\u6210\u6ee1\u8db3\u7279\u5b9a\u8981\u6c42\u7684\u5206\u5b50\u6307\u4ee4\uff0c\u4f8b\u5982\uff1a<\/p>\n

AtomNum \u5b50\u4efb\u52a1\uff1a\u300c\u8bf7\u751f\u6210\u4e00\u4e2a\u542b\u6709 6 \u4e2a\u78b3\u539f\u5b50\u548c 1 \u4e2a\u6c27\u539f\u5b50\u7684\u5206\u5b50\u3002\u300d<\/p>\n

BondNum \u5b50\u4efb\u52a1\uff1a\u300c\u8bf7\u751f\u6210\u4e00\u4e2a\u542b\u6709 10 \u4e2a\u952e\u7684\u5206\u5b50\u3002\u300d<\/p>\n

FunctionalGroup \u5b50\u4efb\u52a1\uff1a\u300c\u8bf7\u751f\u6210\u4e00\u4e2a\u542b\u6709\u82ef\u73af\u548c\u7fa7\u57fa\u7684\u5206\u5b50\u3002\u300d<\/p>\n

\u4efb\u52a1 Prompt: \u5982\u4e0b\u8868\u6240\u793a\uff1a<\/p>\n

<\/a><\/p>\n

\u8bc4\u4f30\u6307\u6807<\/strong><\/p>\n

1. MolEdit \u548c MolOpt \u4efb\u52a1\uff1a<\/p>\n

\u6210\u529f\u7387 (Success Rate): \u901a\u8fc7\u5316\u5b66\u5de5\u5177\u7bb1\uff08\u4f8b\u5982 RDKit\uff09\u81ea\u52a8\u6d4b\u8bd5\u751f\u6210\u7684\u5206\u5b50\u662f\u5426\u6ee1\u8db3\u8981\u6c42\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e MolEdit \u4efb\u52a1\u7684 AddComponent \u5b50\u4efb\u52a1\uff0c\u4f1a\u68c0\u67e5\u751f\u6210\u5206\u5b50\u4e2d\u662f\u5426\u5305\u542b\u6307\u5b9a\u6570\u91cf\u7684\u76ee\u6807\u5b98\u80fd\u56e2\uff1b\u5bf9\u4e8e MolOpt \u4efb\u52a1\u7684 LogP \u5b50\u4efb\u52a1\uff0c\u4f1a\u68c0\u67e5\u751f\u6210\u5206\u5b50\u7684 LogP \u503c\u662f\u5426\u7b26\u5408\u4f18\u5316\u65b9\u5411\u3002<\/p>\n

\u76f8\u4f3c\u6027 (Similarity): \u8bc4\u4f30\u751f\u6210\u5206\u5b50\u4e0e\u539f\u59cb\u5206\u5b50\u4e4b\u95f4\u7684\u76f8\u4f3c\u7a0b\u5ea6\u3002\u4f7f\u7528 Tanimoto \u76f8\u4f3c\u5ea6\u8ba1\u7b97\u65b9\u6cd5\uff0c\u5c06\u5206\u5b50\u8f6c\u6362\u4e3a Morgan \u6307\u7eb9\uff0c\u7136\u540e\u6bd4\u8f83\u6307\u7eb9\u7684\u4ea4\u96c6\u548c\u5e76\u96c6\u3002<\/p>\n

\u6709\u6548\u6027 (Validity): \u8bc4\u4f30\u751f\u6210\u5206\u5b50\u7684\u5316\u5b66\u6709\u6548\u6027\uff0c\u5373\u662f\u5426\u9075\u5faa\u5206\u5b50\u7ed3\u6784\u7684\u8bed\u6cd5\u89c4\u5219\u3002<\/p>\n

2. MolCustom \u4efb\u52a1\uff1a<\/p>\n

\u6210\u529f\u7387 (Success Rate): \u901a\u8fc7\u5316\u5b66\u5de5\u5177\u7bb1\u81ea\u52a8\u6d4b\u8bd5\u751f\u6210\u7684\u5206\u5b50\u662f\u5426\u6ee1\u8db3\u6307\u5b9a\u7684\u539f\u5b50\u6570\u91cf\u3001\u952e\u6570\u91cf\u6216\u5b98\u80fd\u56e2\u8981\u6c42\u3002<\/p>\n

\u65b0\u9896\u6027 (Novelty): \u8bc4\u4f30\u751f\u6210\u5206\u5b50\u4e0e\u73b0\u6709\u5206\u5b50\u4e4b\u95f4\u7684\u5dee\u5f02\u7a0b\u5ea6\u3002\u9009\u62e9 Zinc250K \u6570\u636e\u5e93<\/mark>\u4f5c\u4e3a\u53c2\u8003\uff0c\u8ba1\u7b97\u751f\u6210\u5206\u5b50\u4e0e\u73b0\u6709\u5206\u5b50\u4e4b\u95f4\u7684\u5e73\u5747 Tanimoto \u76f8\u4f3c\u5ea6\uff0c\u5e76\u4ee5\u6b64\u4f5c\u4e3a\u65b0\u9896\u6027\u8bc4\u5206\u3002<\/p>\n

\u6709\u6548\u6027 (Validity): \u4e0e MolEdit \u548c MolOpt \u4efb\u52a1\u76f8\u540c\uff0c\u8bc4\u4f30\u751f\u6210\u5206\u5b50\u7684\u5316\u5b66\u6709\u6548\u6027\u3002<\/p>\n

3. \u5e73\u5747\u52a0\u6743\u6210\u529f\u7387 (Average Weighted Success Rate):<\/p>\n

\u7531\u4e8e\u4ec5\u9760\u6210\u529f\u7387\u65e0\u6cd5\u53cd\u6620\u5206\u5b50\u7f16\u8f91\u548c\u4f18\u5316\u7684\u8fc7\u7a0b\uff08\u5373\u751f\u6210\u7684\u5206\u5b50\u7a76\u7adf\u662f\u57fa\u4e8e\u7ed9\u5b9a\u5206\u5b50\u7f16\u8f91\u800c\u6765\u8fd8\u662f\u4ece\u5934\u751f\u6210\u7684\uff09\uff1b\u540c\u65f6\u65b0\u9896\u6027\u53c8\u662f\u5b9a\u5236\u5206\u5b50\u751f\u6210\u7684\u4e00\u4e2a\u91cd\u8981\u8003\u91cf\u6307\u6807\u3002\u4e3a\u4e86\u7efc\u5408\u8bc4\u4f30 LLM \u5728 TOMG-Bench \u4e0a\u7684\u5e73\u5747\u6027\u80fd\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u5e73\u5747\u52a0\u6743\u6210\u529f\u7387\u6307\u6807\uff0c\u8be5\u6307\u6807\u5c06\u76f8\u4f3c\u6027\u8bc4\u5206\u548c\u65b0\u9896\u6027\u8bc4\u5206\u4f5c\u4e3a\u6210\u529f\u7387\u6743\u91cd<\/mark>\uff0c\u4ee5\u5e73\u8861\u8bc4\u4f30\u7ed3\u679c\u3002<\/p>\n

OpenMolIns \u6307\u4ee4\u5fae\u8c03\u6570\u636e\u96c6<\/strong><\/p>\n

OpenMolIns \u662f\u4e00\u4e2a\u4e13\u95e8\u4e3a TOMG-Bench \u5f00\u53d1\u7684\u6307\u4ee4\u5fae\u8c03\u6570\u636e\u96c6\uff0c\u65e8\u5728\u5e2e\u52a9 LLM \u66f4\u597d\u5730\u7406\u89e3\u548c\u6267\u884c\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u4efb\u52a1\u3002<\/p>\n

1. \u6570\u636e\u6765\u6e90\uff1a<\/p>\n

OpenMolIns \u7684\u6570\u636e\u6765\u6e90\u4e8e PubChem \u6570\u636e\u5e93<\/mark>\uff0c\u800c\u975e Zinc250K \u6570\u636e\u5e93<\/mark>\u3002<\/p>\n

\u4e3a\u4e86\u907f\u514d\u6570\u636e\u6cc4\u6f0f\uff0cOpenMolIns \u6570\u636e\u96c6\u4e2d\u7684\u5206\u5b50\u4e0e Zinc250K \u6570\u636e\u5e93<\/mark>\u4e2d\u7684\u5206\u5b50\u4e92\u4e0d\u91cd\u53e0\u3002<\/p>\n

2. \u6570\u636e\u7ed3\u6784\uff1a<\/p>\n

OpenMolIns \u6570\u636e\u96c6\u6309\u7167\u4e94\u4e2a\u4e0d\u540c\u7684\u6570\u636e\u89c4\u6a21\u8fdb\u884c\u6784\u5efa\uff1a<\/p>\n

\u8f7b (Light): \u5305\u542b 4,500 \u4e2a\u6837\u672c<\/p>\n

\u5c0f (Small): \u5305\u542b 18,000 \u4e2a\u6837\u672c<\/p>\n

\u4e2d (Medium): \u5305\u542b 45,000 \u4e2a\u6837\u672c<\/p>\n

\u5927 (Large): \u5305\u542b 90,000 \u4e2a\u6837\u672c<\/p>\n

\u8d85\u5927 (Xlarge): \u5305\u542b 1,200,000 \u4e2a\u6837\u672c<\/p>\n

\u6bcf\u4e2a\u6570\u636e\u89c4\u6a21\u4e2d\uff0c\u4e5d\u4e2a\u5b50\u4efb\u52a1\u7684\u6837\u672c\u6570\u91cf\u5747\u5300\u5206\u5e03\u3002<\/p>\n

3. \u6570\u636e\u5185\u5bb9\uff1a<\/p>\n

OpenMolIns \u6570\u636e\u96c6\u5305\u542b\u4e5d\u4e2a\u5b50\u4efb\u52a1\u7684\u6307\u4ee4\u548c\u5bf9\u5e94\u7684\u76ee\u6807\u5206\u5b50\u7ed3\u6784\uff1a<\/p>\n

AddComponent, DelComponent, SubComponent (MolEdit): \u6307\u4ee4 LLM \u5bf9\u5206\u5b50\u8fdb\u884c\u6dfb\u52a0\u3001\u5220\u9664\u6216\u66ff\u6362\u5b98\u80fd\u56e2\u7684\u64cd\u4f5c\u3002<\/p>\n

LogP, MR, QED (MolOpt): \u6307\u4ee4 LLM \u4f18\u5316\u5206\u5b50\u7684 LogP\u3001MR \u6216 QED \u503c\u3002<\/p>\n

AtomNum, BondNum, FunctionalGroup (MolCustom): \u6307\u4ee4 LLM \u751f\u6210\u6307\u5b9a\u539f\u5b50\u6570\u91cf\u3001\u952e\u6570\u91cf\u6216\u5b98\u80fd\u56e2\u7684\u5206\u5b50\u3002<\/p>\n

\u6bcf\u4e2a\u6837\u672c\u90fd\u5305\u542b\u4e00\u4e2a\u6307\u4ee4\u548c\u5176\u5bf9\u5e94\u7684\u76ee\u6807\u5206\u5b50\u7ed3\u6784\uff0c\u7528\u4e8e\u6307\u5bfc LLM \u7684\u8bad\u7ec3\u548c\u5fae\u8c03\u3002<\/p>\n

4. \u6570\u636e\u683c\u5f0f\uff1a<\/p>\n

OpenMolIns \u6570\u636e\u96c6\u91c7\u7528 SMILES \u5b57\u7b26\u4e32\u8868\u793a\u5206\u5b50\u7ed3\u6784\u3002<\/p>\n

\u6307\u4ee4\u91c7\u7528\u81ea\u7136\u8bed\u8a00\u6587\u672c\u5f62\u5f0f\uff0c\u63cf\u8ff0\u5bf9\u5206\u5b50\u7684\u64cd\u4f5c\u6216\u8981\u6c42\u3002<\/p>\n

\u901a\u8fc7 OpenMolIns \u6570\u636e\u96c6\uff0cLLM \u53ef\u4ee5\u66f4\u597d\u5730\u5b66\u4e60\u5982\u4f55\u6839\u636e\u6587\u672c\u6307\u4ee4\u751f\u6210\u7b26\u5408\u8981\u6c42\u7684\u5206\u5b50\u7ed3\u6784\uff0c\u4ece\u800c\u63d0\u5347\u5176\u5728 TOMG-Bench \u4e0a\u7684\u6027\u80fd\u3002<\/p>\n

\u5b9e\u9a8c\u7ed3\u679c\u548c\u53d1\u73b0<\/strong><\/p>\n

TOMG-Bench \u7684\u5b9e\u9a8c\u7ed3\u679c\u63ed\u793a\u4e86 LLM \u5728\u5206\u5b50\u53d1\u73b0\u9886\u57df\u5f00\u653e\u57df\u751f\u6210\u80fd\u529b\u7684\u4e00\u4e9b\u91cd\u8981\u53d1\u73b0\uff1a<\/p>\n

1. \u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u4efb\u52a1\u5177\u6709\u6311\u6218\u6027\uff1a\u5373\u4f7f\u662f\u5148\u8fdb\u7684\u79c1\u6709\u6a21\u578b\uff0c\u4f8b\u5982 Claude-3.5 \u548c Gemini-1.5-pro\uff0c\u5728 MolCustom \u4efb\u52a1\u4e0a\u7684\u6210\u529f\u7387\u4e5f\u4f4e\u4e8e 25%\uff0c\u8868\u660e LLM \u5728\u4ece\u96f6\u5f00\u59cb\u751f\u6210\u5206\u5b50\u7ed3\u6784\u65b9\u9762\u4ecd\u5b58\u5728\u8f83\u5927\u6311\u6218\u3002<\/p>\n

2.\u00a0\u5f00\u6e90\u6a21\u578b\u8868\u73b0\u8ffd\u8d76\u8fc5\u901f\uff1a\u5728 TOMG-Bench \u4e0a\uff0c\u5f00\u6e90\u6a21\u578b Llama-3.1-8B-Instruct \u7684\u5e73\u5747\u52a0\u6743\u6210\u529f\u7387\u8d85\u8fc7\u4e86\u6240\u6709\u5f00\u6e90\u901a\u7528 LLM\uff0c\u751a\u81f3\u8d85\u8fc7\u4e86 GPT-3.5-turbo\u3002\u8fd9\u8868\u660e\uff0c\u5373\u4f7f\u6ca1\u6709\u7ecf\u8fc7\u5316\u5b66\u76f8\u5173\u8bed\u6599\u5e93<\/mark>\u7684\u9884\u8bad\u7ec3\uff0c\u5f00\u6e90\u6a21\u578b\u4e5f\u5177\u5907\u8f83\u5f3a\u7684\u5206\u5b50\u7406\u89e3\u548c\u751f\u6210\u80fd\u529b\u3002<\/p>\n

3. \u6a21\u578b\u80fd\u529b\u4e0e\u6027\u80fd\u6b63\u76f8\u5173\uff1a\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u6a21\u578b\u80fd\u529b\u4e0e\u5728 TOMG-Bench \u4e0a\u7684\u6027\u80fd\u5448\u6b63\u76f8\u5173\u3002\u4f8b\u5982\uff0cLlama-3.1-8B-Instruct \u7684\u6027\u80fd\u4f18\u4e8e Llama-3.2-1B-Instruct\uff0c\u800c Llama-3.70B-Instruct \u7684\u6027\u80fd\u53c8\u4f18\u4e8e Llama-3.1-8B-Instruct\u3002\u8fd9\u8868\u660e\uff0c\u66f4\u5927\u7684\u6a21\u578b\u901a\u5e38\u80fd\u591f\u751f\u6210\u66f4\u9ad8\u8d28\u91cf\u7684\u5206\u5b50\u7ed3\u6784\u3002<\/p>\n

4. ChEBI-20 \u6570\u636e\u96c6\u4e0d\u8db3\u4ee5\u8bad\u7ec3 LLM\u8fdb\u884c\u5206\u5b50-\u6587\u672c\u5bf9\u9f50\uff1a\u5c3d\u7ba1 ChEBI-20 \u6570\u636e\u96c6\u5728\u5206\u5b50-\u63cf\u8ff0\u7ffb\u8bd1\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272\uff0c\u4f46\u5b9e\u9a8c\u7ed3\u679c\u663e\u793a\uff0c\u5728 TOMG-Bench \u4e0a\uff0c\u57fa\u4e8e ChEBI-20 \u6570\u636e\u96c6\u5fae\u8c03\u7684 LLM \u6027\u80fd\u8f83\u5dee\u3002\u8fd9\u8868\u660e\uff0cChEBI-20 \u6570\u636e\u96c6\u7f3a\u4e4f\u8db3\u591f\u7684\u591a\u6837\u6027\uff0c\u65e0\u6cd5\u6709\u6548\u5730\u8bad\u7ec3 LLM \u638c\u63e1\u5206\u5b50\u7ed3\u6784\u7684\u590d\u6742\u6027\u548c\u591a\u6837\u6027\u3002<\/p>\n

5. \u6570\u636e\u89c4\u6a21\u5bf9\u6027\u80fd\u7684\u5f71\u54cd\uff1a\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u6570\u636e\u89c4\u6a21\u5bf9 LLM \u5728 TOMG-Bench \u4e0a\u7684\u6027\u80fd\u6709\u663e\u8457\u5f71\u54cd\u3002\u4f8b\u5982\uff0cGalactica-125M \u5728 OpenMolIns-xlarge \u6570\u636e\u96c6\u4e0a\u7684\u8868\u73b0\u4f18\u4e8e Llama3-70B-Instruct\uff0c\u8fd9\u8868\u660e\u66f4\u5927\u7684\u6570\u636e\u96c6\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u5347 LLM \u7684\u6027\u80fd\u3002<\/p>\n

6. TOMG-Bench \u53ef\u4ee5\u53cd\u6620\u5927\u8bed\u8a00\u6a21\u578b<\/mark>\u7684\u9886\u57df\u6cdb\u5316\u80fd\u529b\uff0c\u5c3d\u7ba1\u73b0\u6709\u7684LLM\u5728\u4e00\u4e9b\u73b0\u6709\u7684 benchmark \u4e0a\uff08\u5982\u6570\u5b66\uff09\u8868\u73b0\u51fa\u8272\uff0c\u4f46\u662f\u4ed6\u4eec\u53ef\u80fd\u662f\u300c\u504f\u79d1\u751f\u300d\uff1a\u80fd\u529b\u96be\u4ee5\u6cdb\u5316\u5230\u9884\u8bad\u7ec3\u4e2d\u6240\u6b20\u7f3a\u7684\u4efb\u52a1\u4e0a\uff0c\u8fd9\u79cd\u300c\u504f\u79d1\u300d\u5f88\u6709\u53ef\u80fd\u5f71\u54cd\u7528\u6237\u5b9e\u9645\u4f7f\u7528 LLM \u7684\u4f53\u9a8c\u3002\u56e0\u6b64\uff0cTOMG-Bench \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4e5f\u80fd\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u73b0\u6709\u6a21\u578b\u7684\u4e0d\u8db3\u4e4b\u5904\uff0c\u5e76\u63d0\u4f9b\u4e86\u6539\u8fdb\u601d\u8def\u3002<\/p>\n

Leaderboard<\/strong><\/p>\n

\u5728TOMG-Bench \u7684\u4e0a\u6392\u540d\u9760\u524d\u7684\u6a21\u578b\u5982\u4e0b\uff08\u524d\u4e94\u540d\uff09\uff1a<\/p>\n

Claude-3.5: \u5e73\u5747\u6210\u529f\u7387\u4e3a 51.10%\uff0c\u52a0\u6743\u6210\u529f\u7387\u4e3a 35.92%\u3002<\/p>\n

Gemini-1.5-pro: \u5e73\u5747\u6210\u529f\u7387\u4e3a 52.25%\uff0c\u52a0\u6743\u6210\u529f\u7387\u4e3a 34.80%\u3002<\/p>\n

GPT-4-turbo: \u5e73\u5747\u6210\u529f\u7387\u4e3a 50.74%\uff0c\u52a0\u6743\u6210\u529f\u7387\u4e3a 34.23%\u3002<\/p>\n

GPT-4o: \u5e73\u5747\u6210\u529f\u7387\u4e3a 49.08%\uff0c\u52a0\u6743\u6210\u529f\u7387\u4e3a 32.29%\u3002<\/p>\n

Claude-3: \u5e73\u5747\u6210\u529f\u7387\u4e3a 46.14%\uff0c\u52a0\u6743\u6210\u529f\u7387\u4e3a 30.47%\u3002<\/p>\n

Llama-3.1-8B (OpenMolIns-large): \u6392\u540d\u7b2c\u516d\uff0c\u5e73\u5747\u6210\u529f\u7387\u4e3a 43.1%\uff0c\u52a0\u6743\u6210\u529f\u7387\u4e3a 27.22%\u3002\u8fd9\u8868\u660e OpenMolIns \u6570\u636e\u96c6\u80fd\u591f\u6709\u6548\u5730\u63d0\u5347 LLM \u5728\u5206\u5b50\u751f\u6210\u4efb\u52a1\u4e0a\u7684\u6027\u80fd\u3002<\/p>\n

<\/a><\/p>\n

\u56fe 3: TOMG-Bench \u7684Leaderboard<\/p>\n

\u603b\u7ed3<\/strong><\/p>\n

TOMG-Bench \u662f\u7b2c\u4e00\u4e2a\u7528\u4e8e\u8bc4\u4f30\u5927\u578b\u8bed\u8a00\u6a21\u578b<\/mark> (LLM) \u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u80fd\u529b\u7684\u57fa\u51c6<\/mark>\u3002\u5176\u5305\u542b\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u5305\u542b\u4e09\u4e2a\u4e3b\u8981\u4efb\u52a1\uff1a\u5206\u5b50\u7f16\u8f91 (MolEdit)\u3001\u5206\u5b50\u4f18\u5316 (MolOpt) \u548c\u5b9a\u5236\u5206\u5b50\u751f\u6210 (MolCustom)\u3002<\/p>\n

\u6bcf\u4e2a\u4efb\u52a1\u8fdb\u4e00\u6b65\u5305\u542b\u4e09\u4e2a\u5b50\u4efb\u52a1\uff0c\u6bcf\u4e2a\u5b50\u4efb\u52a1\u5305\u542b 5,000 \u4e2a\u6d4b\u8bd5\u6837\u672c\u3002\u9274\u4e8e\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u7684\u56fa\u6709\u590d\u6742\u6027\uff0c\u4e00\u4e2a\u81ea\u52a8\u5316\u8bc4\u4f30\u7cfb\u7edf\u5c06\u7528\u4e8e\u8861\u91cfLLM\u751f\u6210\u7684\u5206\u5b50\u8d28\u91cf\u3002<\/p>\n

\u5bf9 25 \u4e2a LLM \u7684\u7efc\u5408\u57fa\u51c6<\/mark>\u6d4b\u8bd5\u63ed\u793a\u4e86\u6587\u672c\u5f15\u5bfc\u5206\u5b50\u53d1\u73b0\u4e2d\u5f53\u524d\u7684\u9650\u5236\u548c\u6f5c\u5728\u7684\u6539\u8fdb\u9886\u57df\u3002<\/p>\n

\u6b64\u5916\uff0c\u5728 OpenMolIns \u6307\u4ee4\u5fae\u8c03\u6570\u636e\u96c6\u7684\u5e2e\u52a9\u4e0b\uff0cLlama3.1-8B \u80fd\u591f\u4f18\u4e8e\u6240\u6709\u5f00\u6e90\u901a\u7528 LLM\uff0c\u751a\u81f3\u5728 TOMG-Bench \u4e0a\u6bd4 GPT-3.5-turbo \u9ad8\u51fa 46.5%\u3002<\/p>\n

\u8fd9\u91cc\u7684\u6d4b\u8bd5\u811a\u672c\u548c\u6570\u636e\u96c6\u5747\u5df2\u5f00\u6e90\uff0c\u6b22\u8fce\u5927\u5bb6\u6765\u5c1d\u8bd5 TOMG-Bench\uff0c\u4e5f\u6b22\u8fce\u5927\u5bb6\u6765\u5237\u699c\uff01<\/p>\n

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:TOMG-Bench\uff1a\u5927\u8bed\u8a00\u6a21\u578b\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u65b0\u57fa\u51c6<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:TOMG-Bench\uff1a […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[27],"tags":[71,55],"class_list":["post-38583","post","type-post","status-publish","format-standard","hentry","category-news","tag-rag","tag-55"],"yoast_head":"\nTOMG-Bench\uff1a\u5927\u8bed\u8a00\u6a21\u578b\u5f00\u653e\u57df\u5206\u5b50\u751f\u6210\u65b0\u57fa\u51c6 - 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