5\u6708\u306e\u5b66\u5185\u5171\u540c\u7814\u7a76\u4fc3\u9032\u30c8\u30fc\u30af\u306f\u3001\u8a08\u7b97\u30b7\u30b9\u30c6\u30e0\u30ba\u751f\u7269\u5b66\u7814\u7a76\u5ba4\u306e\u5c0f\u91ce\u76f4\u4eae\u5148\u751f\u306b\u3054\u8b1b\u6f14\u3044\u305f\u3060\u304d\u307e\u3057\u305f\u3002<\/p>\n
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====================<\/p>\n
\u5c0f\u91ce\u76f4\u4eae \u51c6\u6559\u6388 (\u8a08\u7b97\u30b7\u30b9\u30c6\u30e0\u30ba\u751f\u7269\u5b66\u7814\u7a76\u5ba4)<\/p>\n
TITLE: Embedding Natural Organic Compounds into Latent Space by Autoencoder Using Molecular Hypergraph Grammar<\/p>\n
Abstract:<\/p>\n
We propose a model for embedding natural organic compounds into a latent space using a molecular hypergraph grammar-based autoencoder.
\nThe goal is to capture the inherent structural features and chemical properties of organic compounds in a compact and meaningful representation. The encoder maps the input compounds, represented as hypergraphs, into a lower-dimensional latent space.
\nTo evaluate the effectiveness of our approach, we construct a classifier of the organic compounds into manually annotated hierarchical categories such as Terpenoids-Triterpenoids-Limonoids, etc.
\nThis embedding enables various downstream applications, such as compound clustering, similarity searching, and property prediction.<\/p>\n","protected":false},"excerpt":{"rendered":"
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