Directional Message Passing on Molecular Graphs via Synthetic Coordinates

Johannes Klicpera, Chandan Yeshwanth, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

15 Zitate (Scopus)

Abstract

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 molecular configurations, and graph-based distances using a symmetric variant of personalized PageRank. To leverage both distance and angular information we propose a method of transforming normal graph neural networks into directional MPNNs. We show that with this transformation we can reduce the error of a normal graph neural network by 55% on the ZINC benchmark. We furthermore set the state of the art on ZINC and coordinate-free QM9 by incorporating synthetic coordinates in the SMP and DimeNet++ models. Our implementation is available online. 1.

OriginalspracheEnglisch
TitelAdvances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
Redakteure/-innenMarc'Aurelio Ranzato, Alina Beygelzimer, Yann Dauphin, Percy S. Liang, Jenn Wortman Vaughan
Herausgeber (Verlag)Neural information processing systems foundation
Seiten15421-15433
Seitenumfang13
ISBN (elektronisch)9781713845393
PublikationsstatusVeröffentlicht - 2021
Veranstaltung35th Conference on Neural Information Processing Systems, NeurIPS 2021 - Virtual, Online
Dauer: 6 Dez. 202114 Dez. 2021

Publikationsreihe

NameAdvances in Neural Information Processing Systems
Band19
ISSN (Print)1049-5258

Konferenz

Konferenz35th Conference on Neural Information Processing Systems, NeurIPS 2021
OrtVirtual, Online
Zeitraum6/12/2114/12/21

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