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- W1673784558 abstract "Accurately predicting the endpoints of chemical compounds is an important step towards drug design and molecular screening in particular. Here we develop a recursive architecture that is capable of mapping Undirected Graphs into individual labels, and apply it to the prediction of a number of different properties of small molecules. The results we obtain are generally state-of-the-art. The final model is completely general and may be applied not only to prediction of molecular properties, but to a vast range of problems in which the input is a graph and the output is either a single property or (with small modifications) a set of properties of the nodes." @default.
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- W1673784558 date "2009-01-01" @default.
- W1673784558 modified "2023-09-25" @default.
- W1673784558 title "Recursive Neural Networks for Undirected Graphs for Learning Molecular Endpoints" @default.
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- W1673784558 doi "https://doi.org/10.1007/978-3-642-04031-3_34" @default.
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