Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the encoding process, however, existing flow models either require artifactual dequantization or specific node/edge orderings, lack desiderata such as permutation invariance, or induce discrepancy between the encoding and the decoding steps that necessitates post hoc validity correction. We circumvent these issues with novel continuous normalizing E(3)-equivariant flows, based on a system of node ODEs coupled as a graph PDE, that repeatedly reconcile locally toward globally aligned densities. Our models can be cast as message-passing temporal networks, and result in superlative performance on the tasks of density estimation and molecular generation. In particular, our generated samples achieve state-of-the-art on both the standard QM9 and ZINC250K benchmarks.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 35 (NeurIPS 2022)
EditorsS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
PublisherMorgan Kaufmann Publishers
Number of pages13
ISBN (Print)978-1-7138-7108-8
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventConference on Neural Information Processing Systems - New Orleans, United States
Duration: 28 Nov 20229 Dec 2022
Conference number: 36

Publication series

NameAdvances in Neural Information Processing Systems
PublisherMorgan Kaufmann Publishers
ISSN (Print)1049-5258


ConferenceConference on Neural Information Processing Systems
Abbreviated titleNeurIPS
Country/TerritoryUnited States
CityNew Orleans
Internet address


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