Directional Message Passing on Molecular Graphs via Synthetic Coordinates. (arXiv:2111.04718v2 [cs.LG] UPDATED)

Graph neural networks that leverage coordinates via directional message
passing have recently set the state of the art on multiple molecular property
prediction tasks. However, they rely on atom position information that is often
unavailable, and obtaining it is usually prohibitively expensive or even
impossible. In this paper we propose synthetic coordinates that enable the use
of advanced GNNs without requiring the true molecular configuration. We propose
two distances as synthetic coordinates: Distance bounds that specify the rough
range of …

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