featuregood first issuehelp wantednn
Métricas do repositório
- Stars
- (19.985 stars)
- Métricas de merge de PR
- (Mesclagem média 16d 3h) (13 fundiu PRs em 30d)
Description
🚀 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
\begin{aligned}
\mathbf{a^{(k)}_{u}} &= AGG^{(k)}_G\left( \{ (\mathbf{h^{(k-1)}_u}, \mathbf{h^{(k-1)}_v}, \mathbf{h^{(k-1)}_{uv}} : v \in \mathit{N}(u) \}\right)\\
\mathbf{h^{(k)}_{u}} &= COMBINE^{(k)}_G (\mathbf{h^{(k-1)}_{u}}, \mathbf{a^{(k)}_{u}})
\end{aligned}
and for graph $H$ (the bond-bond angle graph) it looks like
\begin{aligned}
\mathbf{a^{(k)}_{uv}} &= AGG^{(k)}_H\left( \{ (\mathbf{h^{(k-1)}_{uv}}, \mathbf{h^{(k-1)}_{uw}}, \mathbf{x_{wuv}}) : w \in \mathit{N}(u) \} \right. \notag \\
& \qquad \qquad \qquad \left. \cup \{ (\mathbf{h^{(k-1)}_{uv}}, \mathbf{h^{(k-1)}_{vw}}, \mathbf{x_{uvw}}) : w \in \mathit{N}(v) \}\right)\\
\mathbf{h^{(k)}_{uv}} &= COMBINE^{(k)}_H (\mathbf{h^{(k-1)}_{uv}}, \mathbf{a^{(k)}_{uv}})\\
\end{aligned}
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