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In short, they propose a novel architecture called GeoGNN which encodes additional spatial information about molecular geometries by modeling both atom to bond and bond to bond angle relations. They do so by creating two graphs, one for each relation type as shown below
The message passing for graph $G$ (the atom-bond graph) looks like
馃殌 The feature, motivation and pitch
I'd like to implement the paper Geometry-enhanced molecular representation learning for property prediction by Fang et al.
In short, they propose a novel architecture called GeoGNN which encodes additional spatial information about molecular geometries by modeling both atom to bond and bond to bond angle relations. They do so by creating two graphs, one for each relation type as shown below
The message passing for graph$G$ (the atom-bond graph) looks like
and for graph$H$ (the bond-bond angle graph) it looks like
They show it does quite well on the MoleculeNet dataset. Would it be possible to contribute it to PyG?
Alternatives
There's a repo here written in
paddle
.Additional context
No response
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