GraphML News (Jan 27th) - New Blogs, LigandMPNN is available

Seems like everyone is grinding for the ICML’24 deadline next week so there isn’t much news those days. A few highlights:

Dimension Research published 2/3 parts of their ML x Bio review of NeurIPS’23: on Generative Protein Design, and on Generative Molecular Design, the last one is going to be about drug target interaction prediction.

The blog post on Exphormer by Ameya Velingker and Balaji Venkatachalam from Google Research on the neat ICML’23 sparse graph transformer architecture that scales to graphs much larger than molecules. Glad to see GraphGPS and Long Range Graph Benchmark mentioned a few times 🙂

LigandMPNN was released on GitHub this week after appearing as a module in several recent protein generation papers. LigandMPNN significantly improves over ProteinMPNN in modeling non-protein components like small molecules, metals, and nucleotides.

Weekend reading:

Equivariant Graph Neural Operator for Modeling 3D Dynamics by Minkai Xu, Jiaqi Han feat Jure Leskovec and Stefano Ermon: equivariant GNNs 🤝 neural operators, also provides a nice condensed intro to the topic

Towards Principled Graph Transformers by Luis Müller and Christopher Morris - study of the Edge Transformer with triangular attention applied to graph tasks. Edge Transformer has shown remarkable systematic generalization capabilities and it’s intriguing to see how it works on graphs (O(N^3) complexity for now though).

Tweets to Citations: Unveiling the Impact of Social Media Influencers on AI Research Visibility - turns out that papers shared on X / Twitter by AK and Aran Komatsuzaki have significantly more citations. Time to revive your old sci-Twitter account