{"id":24410,"date":"2021-03-12T21:21:23","date_gmt":"2021-03-12T13:21:23","guid":{"rendered":"https:\/\/swarma.org\/?p=24410"},"modified":"2021-03-12T21:21:23","modified_gmt":"2021-03-12T13:21:23","slug":"%e5%9b%be%e8%a1%a8%e7%a4%ba%e6%96%b9%e6%b3%95%e9%a9%b1%e5%8a%a8%e5%a4%a7%e5%88%86%e5%ad%90%e8%ae%a1%e7%ae%97%e7%a0%94%e7%a9%b6-arxiv%e9%80%9f%e9%80%92","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=24410","title":{"rendered":"\u56fe\u8868\u793a\u65b9\u6cd5\u9a71\u52a8\u5927\u5206\u5b50\u8ba1\u7b97\u7814\u7a76 | arXiv\u901f\u9012"},"content":{"rendered":"
<\/p>\n

\u5bfc\u8bed<\/p>\n

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\u9ec4\u9756\u946b | \u4f5c\u8005
DrugAI | \u6765\u6e90<\/p>\n


\u8bba\u6587\u9898\u76ee\uff1a
Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2103.02565<\/p>\n

\u4ecb\u7ecd<\/p>\n

\u751f\u7269\u5927\u5206\u5b50\u6784\u6210\u4e86\u751f\u547d\u7684\u57fa\u7840\uff0c\u662f\u751f\u5b58\u548c\u751f\u957f\u7684\u5f3a\u5927\u52a8\u529b\u3002\u5355\u4e2a\u5927\u5206\u5b50\u662f\u7531\u5355\u4f53\uff0c\u7528\u4e8e\u8fde\u63a5\u7684\u952e\u53ca\u7a7a\u95f4\u6392\u5217\u7ec4\u5408\u800c\u6210\u3002\u7814\u7a76\u4eba\u5458\u901a\u8fc7\u6539\u53d8\u5355\u4f53\uff0c\u952e\u548c\u62d3\u6251\u7ed3\u6784\uff08\u7ebf\u6027\u548c\u975e\u7ebf\u6027\uff0c\u5982\u652f\u94fe\u5f62\uff0c\u661f\u5f62\u548c\u74f6\u5237\u5f62\uff09\u63a2\u7d22\u4e86\u5e7f\u9614\u7684\u5316\u5b66\u7a7a\u95f4\u3002\u4e0e\u7528\u4e8e\u8868\u793a\u5c0f\u5206\u5b50\u7684SMILES\u7c7b\u4f3c\uff0c\u5927\u5206\u5b50\u53ef\u4ee5\u7528\u7ebf\u6027\u8868\u793a\u6cd5\u8868\u793a\u3002\u4f46\u5982\u86cb\u767d\u8d28\u548cDNA\/RNA\u7684\u7ebf\u6027\u751f\u7269\u5927\u5206\u5b50\u4f8b\u5916\uff0c\u5b83\u4eec\u901a\u5e38\u7528\u5355\u4e2a\u6216\u4e09\u4e2a\u5b57\u6bcd\u7684\u5355\u4f53\u7f16\u7801\u7684\u5e8f\u5217\u8868\u793a\u3002\u4f46\u8fd9\u4e9b\u65b9\u5f0f\u5e38\u5e38\u53d7\u5230\u5176\u5316\u5b66\u7a7a\u95f4\u7684\u8986\u76d6\u8303\u56f4\u4ee5\u53ca\u652f\u6301\u6240\u6709\u62d3\u6251\u7ed3\u6784\u7684\u80fd\u529b\u7684\u9650\u5236\uff0c\u5e76\u4e14\u5e38\u5e38\u8981\u6839\u636e\u5355\u4f53\u7684\u4e0d\u540c\u800c\u91cd\u65b0\u8bbe\u8ba1\u8868\u793a\u65b9\u5f0f\u3002<\/p>\n

\u8fd1\u5e74\u6765\uff0c\u6709\u8bb8\u591a\u5229\u7528\u5e8f\u5217\u6bd4\u5bf9\uff0c\u7f16\u8f91\u8ddd\u79bb\uff0c\u7ebf\u6027\u6838\u548c\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u8ba1\u7b97\u5927\u5206\u5b50\u76f8\u4f3c\u6027\u7684\u5de5\u4f5c\uff0c\u4f46\u8fd9\u4e9b\u65b9\u6cd5\u4ec5\u9650\u4e8e\u751f\u7269\u5927\u5206\u5b50\uff0c\u4e0d\u80fd\u6269\u5c55\u5230\u4e00\u822c\u7684\u5927\u5206\u5b50\u5316\u5b66\u7a7a\u95f4\uff0c\u5e76\u4e14\u73b0\u6709\u7684\u7528\u4e8e\u751f\u7269\u5927\u5206\u5b50\u7684\u5de5\u5177\u4e0d\u5141\u8bb8\u63ba\u5165\u975e\u5929\u7136\u5355\u4f53\uff0c\u9664\u4e86\u805a\u7cd6\u7279\u5f02\u6027\u5de5\u5177\u4e4b\u5916\uff0c\u4e5f\u4e0d\u80fd\u5904\u7406\u975e\u7ebf\u6027\u62d3\u6251\u7ed3\u6784\u3002<\/p>\n

\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u4f5c\u8005\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u8868\u793a\u5927\u5206\u5b50\u7684\u56fe\u8868\u793a\u65b9\u6cd5\uff0c\u4f7f\u7528\u5e26\u6709Tanimoto\u5316\u5b66\u76f8\u4f3c\u6027\u77e9\u9635\u548c\u4f20\u64ad\u56fe\u6838(propagation graph kernels)\u7684\u56fe\u7f16\u8f91\u8ddd\u79bb(graph edit distances\uff0cGEDs)\u6765\u8ba1\u7b97\u56fe\u76f8\u4f3c\u6027\u3002\u6b64\u5916\uff0c\u8fd8\u9488\u5bf9\u4e0d\u540c\u7684\u4efb\u52a1\u8bad\u7ec3\u4e86\u4e00\u5957\u56fe\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u5728\u805a\u7cd6\u6570\u636e\u96c6\u4e0a\u53d6\u5f97\u4e86\u6700\u597d\u7684\u7ed3\u679c\u3002<\/p>\n


\u6a21\u578b<\/p>\n

\u4f5c\u8005\u4f7f\u7528\u901a\u7528\u6587\u672c\u6587\u4ef6\u683c\u5f0f\u5c06\u5927\u5206\u5b50\u7ed3\u6784\u8f6c\u6362\u4e3a\u673a\u5668\u53ef\u8bfb\u683c\u5f0f\u3002\u6587\u672c\u6587\u4ef6\u5305\u542b3\u4e2a\u90e8\u5206-SMILES\uff0cMONOMERS\u548cBONDS\u3002\u7136\u540e\u5c06\u5927\u5206\u5b50\u89e3\u6790\u6210\u5e26\u6709\u8282\u70b9\u548c\u8fb9\u5c5e\u6027\u7684NetworkX\u56fe\uff0c\u4f7f\u7528\u7acb\u4f53\u5316\u5b66\u6269\u5c55\u8fde\u901a\u6027\u6307\u7eb9\u5bf9\u5355\u4f53\u548c\u952e\u5206\u5b50\u8fdb\u884c\u7279\u5f81\u5316\u3002\u8fd9\u79cd\u8868\u793a\u65b9\u5f0f\u4f7f\u5927\u5206\u5b50\u5728\u5176\u539f\u751f\u72b6\u6001\u4e0b\u7684\u7acb\u4f53\u5316\u5b66\u548c\u62d3\u6251\u7684\u660e\u786e\u529f\u80fd\u5316\u7684\u63cf\u8ff0\u6210\u4e3a\u53ef\u80fd\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5355\u4e00\u7684\u6846\u67b6\u6765\u8868\u793a\u5929\u7136\u548c\u5408\u6210\u3001\u7ebf\u6027\u548c\u975e\u7ebf\u6027\u5927\u5206\u5b50\u3002<\/p>\n


\u56fe1 \u5927\u5206\u5b50\u7684\u56fe\u8868\u793a<\/p>\n

\u5229\u7528\u8fd9\u79cd\u72ec\u7279\u7684\u8868\u793a\u65b9\u5f0f\uff0c\u4f5c\u8005\u4f7f\u7528\u7cbe\u786e\u7684GED\u8bc4\u5206\u4e0eTanimoto\u76f8\u4f3c\u6027\u66ff\u6362\u77e9\u9635\uff0c\u4ee5\u53ca\u56fe\u6838\uff0c\u6765\u8ba1\u7b972\u4e2a\u6216\u591a\u4e2a\u5927\u5206\u5b50\u56fe\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\uff08\u56fe1B\uff09\u3002GED\u901a\u8fc7\u5206\u914d\u8282\u70b9\u548c\u8fb9\u66ff\u6362\u7684\u5206\u6570\u6765\u8ba1\u7b97\u4e24\u4e2a\u56fe\u4e4b\u95f4\u7684\u76f8\u4f3c\u5ea6\u3002\u5e76\u4e14\u4f7f\u7528Tanimoto\u76f8\u4f3c\u5ea6\u77e9\u9635\u4ee3\u66ff\u57fa\u4e8e\u8fdb\u5316\u7edf\u8ba1\u7684\u66ff\u6362\u77e9\u9635\uff0c\u8ba1\u7b97\u5206\u5b50\u6307\u7eb9\u4e4b\u95f4\u7684\u76f8\u4f3c\u5ea6\uff08\u56fe2A\uff0cB\uff09\u3002\u4e3a\u4e86\u89e3\u51b3\u8ba1\u7b97GED\u6210\u672c\u9ad8\u7684\u95ee\u9898\uff0c\u4f5c\u8005\u4f7f\u7528\u4f20\u64ad\u5c5e\u6027\u6838\u6765\u83b7\u5f97\u5927\u578b\u6570\u636e\u96c6\u7684\u76f8\u4f3c\u5ea6\u77e9\u9635\u3002\u6b64\u5916\uff0c\u4f5c\u8005\u8fd8\u5206\u6790\u4e86\u805a\u7cd6\u6570\u636e\u96c6\u7684\u76f8\u4f3c\u6027\u5411\u91cf\u3002\u5728GraKeL\u4e2d\u5b9e\u73b0\u7684\u4f20\u64ad\u5c5e\u6027\u6838\u7528\u4e8e\u8ba1\u7b97\u76f8\u4f3c\u5ea6\uff08\u56fe2C\uff09\u3002\u8be5\u6838\u4e3a\u5927\u5206\u5b50\u56fe\u63d0\u4f9b\u4e86\u7edd\u4f73\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u4eec\u6355\u83b7\u4e86\u672c\u5730\u8282\u70b9\u4fe1\u606f\u5e76\u6cbf\u8fb9\u7f18\u8fed\u4ee3\u5730\u4f20\u64ad\u4e86\u8fd9\u4e9b\u4fe1\u606f\u3002\u4ee5\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6838\u6355\u83b7\u4e86\u5927\u5206\u5b50\u7684\u5c40\u90e8\u5355\u4f53\u5316\u5b66\u548c\u6574\u4f53\u62d3\u6251\u3002<\/p>\n


\u56fe2 \u805a\u7cd6\u5177\u6709\u5e7f\u6cdb\u7684\u5316\u5b66\u76f8\u4f3c\u6027<\/p>\n

\u4f5c\u8005\u63d0\u51fa\u964d\u7ef4\u66f4\u591a\u5730\u53d7\u5206\u7c7b\u5b66\u5206\u7c7b\u7684\u5f71\u54cd\uff0c\u800c\u4e0d\u662f\u514d\u75ab\u539f\u6027\u7684\u5f71\u54cd\u3002\u5728\u56fe2D\u4e2d\uff0c\u4f5c\u8005\u6309\u57df\u7740\u8272\u7ed8\u5236\uff0c\u53ef\u4ee5\u89c2\u5bdf\u5230\u57df\u7684\u6392\u5217\u4e0e\u8fdb\u5316\u8fc7\u7a0b\u76f8\u4f3c\uff0c\u4ece\u4e2d\u5fc3\u7684\u7ec6\u83cc\u5f00\u59cb\uff0c\u7136\u540e\u662f\u771f\u6838\u751f\u7269\uff0c\u7136\u540e\u5728\u8fb9\u7f18\u51fa\u73b0\u75c5\u6bd2\uff0c\u53ef\u4ee5\u770b\u51fa\uff0c\u514d\u75ab\u539f\u6027\u662f\u5c5e\u4e8e\u7279\u5b9a\u7ed3\u6784\u57df\u7684\u805a\u7cd6\u7684\u7ed3\u679c\uff0c\u4f8b\u5982\u7ec6\u83cc\u5177\u6709\u514d\u75ab\u539f\u6027\u3002<\/p>\n


\u5b9e\u9a8c<\/p>\n

\u4f5c\u8005\u9488\u5bf9\u6307\u7eb9\uff0cone-hot\u8282\u70b9\u548c\u8fb9\u5c5e\u6027\u8bad\u7ec3\u4e865\u79cdGNN\u6a21\u578b\u67b6\u6784\uff0c\u4ee5\u9488\u5bf9\u514d\u75ab\u539f\u6027\u548c8\u4e2a\u5206\u7c7b\u6807\u51c6\u5bf9\u805a\u7cd6\u8fdb\u884c\u5206\u7c7b\u3002\u5bf9\u4e8e\u6bcf\u4e2a\u4efb\u52a1\uff0c\u4f5c\u8005\u8bc4\u4f30\u4e86\u5206\u7c7b\u6307\u6807\uff0c\u8be5\u5206\u7c7b\u6307\u6807\u901a\u8fc7\u5bf9\u81f3\u5c11\u5177\u67095\u4e2a\u968f\u673a\u521d\u59cb\u5316\u79cd\u5b50\u7684\u524d5\u4e2a\u8d85\u53c2\u6570\u96c6\u91cd\u65b0\u8bad\u7ec3\u7684\u6a21\u578b\u8fdb\u884c\u5e73\u5747\u5316\u83b7\u5f97\uff08\u88681\uff09\u3002\u5b9e\u9a8c\u8868\u660e\uff0c\u8be5\u6a21\u578b\u83b7\u5f97\u4e86\u6700\u597d\u7684\u7ed3\u679c\uff0c\u5e76\u4e14\u8868\u73b0\u4f18\u4e8e\u6587\u732e\u62a5\u9053\u7684\u6307\u6807\u3002<\/p>\n

\u88681 \u5728\u6d4b\u8bd5\u6570\u636e\u96c6\u4e0a\u83b7\u5f97\u6700\u4f73\u6a21\u578b\u5c5e\u6027\u7ec4\u5408\u7684\u5ea6\u91cf
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\u603b\u7ed3<\/p>\n

\u5927\u5206\u5b50\u56fe\u8868\u793a\u4e0e\u5206\u5b50\u6307\u7eb9\u3001\u56fe\u76f8\u4f3c\u6027\u548cGNN\u76f8\u7ed3\u5408\uff0c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u8868\u793a\u3001\u8ba1\u7b97\u76f8\u4f3c\u6027\u548c\u673a\u5668\u5b66\u4e60\u5927\u5206\u5b50\u7684\u6846\u67b6\u3002\u8fd9\u9879\u5de5\u4f5c\u4e3a\u5927\u5206\u5b50\u7684\u8ba1\u7b97\u7814\u7a76\u63d0\u4f9b\u4e86\u4e00\u79cd\u5316\u5b66\u65b9\u6cd5\u3002\u5728\u4e0d\u4e45\u7684\u5c06\u6765\uff0c\u4f5c\u8005\u5c06\u5728\u5305\u62ec\u86cb\u767d\u8d28\u548cDNA \/ RNA\u5728\u5185\u7684\u5404\u79cd\u5927\u5206\u5b50\u6570\u636e\u96c6\u4e0a\u9a8c\u8bc1\u8be5\u6a21\u578b\u7684\u9002\u7528\u6027\u3002<\/p>\n

\u53c2\u8003\u8d44\u6599
Mohapatra S, An J, G\u00f3mez-Bombarelli R. Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning[J]. arXiv preprint arXiv:2103.02565, 2021.<\/p>\n

\u590d\u6742\u79d1\u5b66\u6700\u65b0\u8bba\u6587<\/p>\n

\u96c6\u667a\u6591\u56fe\u9876\u520a\u8bba\u6587\u901f\u9012\u680f\u76ee\u4e0a\u7ebf\u4ee5\u6765\uff0c\u6301\u7eed\u6536\u5f55\u6765\u81eaNature\u3001Science\u7b49\u9876\u520a\u7684\u6700\u65b0\u8bba\u6587\uff0c\u8ffd\u8e2a\u590d\u6742\u7cfb\u7edf\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u8ba1\u7b97\u793e\u4f1a\u79d1\u5b66\u7b49\u9886\u57df\u7684\u524d\u6cbf\u8fdb\u5c55\u3002\u73b0\u5728\u6b63\u5f0f\u63a8\u51fa\u8ba2\u9605\u529f\u80fd\uff0c\u6bcf\u5468\u901a\u8fc7\u5fae\u4fe1\u670d\u52a1\u53f7\u300c\u96c6\u667a\u6591\u56fe\u300d\u63a8\u9001\u8bba\u6587\u4fe1\u606f\u3002\u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\u5373\u53ef\u4e00\u952e\u8ba2\u9605\uff1a
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\u63a8\u8350\u9605\u8bfb
\u4e3a\u4ec0\u4e48\u795e\u7ecf\u7f51\u7edc\u96be\u4ee5\u7406\u89e3\u751f\u547d\u6e38\u620f\uff1f<\/a><\/p>\n

Nature \u5b50\u520a\uff1a\u673a\u5668\u5b66\u4e60\u9ad8\u7ef4\u6570\u636e\u5206\u6790\u4e2d\u90a3\u4e9b\u4e00\u5b9a\u53ef\u4ee5\u907f\u5f00\u7684\u5751\uff01<\/a><\/p>\n

Nature\u901a\u8baf\uff1a\u5224\u522b\u795e\u7ecf\u7f51\u7edc\u95f4\u7684\u4e2a\u4f53\u5dee\u5f02\u2014\u2014\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u65b0\u5de5\u5177<\/a><\/p>\n

PM2.5-GNN \u56fe\u7f51\u7edc\u96fe\u973e\u9884\u62a5\u6a21\u578b\uff1a\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u5728\u5927\u6c14\u6c61\u67d3\u4e2d\u7684\u5e94\u7528<\/a><\/p>\n

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