Post #765

Graph ML News, April 8th edition - MoML’23, GLB’23, and more Molecular Machine Learning Conference (MoML) 2023 is going to take place at Mila in Montreal on May 29th. MoML is the premier venue for ML applications in drug discovery, quantum chemistry, molecular dynamics, and protein design. Confirmed speakers are Yoshua Bengio (Mila), Djork-Arné Clevert (Pfizer), Marinka Zitnik (Harvard), Gregory Bowman (UPenn), Mohammed AlQuraishi (Columbia), and Dominique Beaini (Mila, Valence Discovery). Posters submission deadline is April 24th, The ‘22 event was held at MIT and was a huge success! In this context, University of Amsterdam (UvA) announced 4 open postdoc positions in the new program on AI 4 Molecules & Materials. The Workshop on Graph Learning Benchmarks (GLB’23) will be held in conjunction with KDD 2023 in Long Beach (California) on Aug 7th. Submit your works on new graph datasets, benchmarks, and software until May 26th. The workshop is non-archival. PyG expands the range of supported hardware to Graphcore IPUs with examples on training temporal GNNs, molecular property prediction GNNs, and inductive KG reasoning GNNs on IPUs. Following up on that, you might want to attend the GNN meetup organized by Graphcore and Kumo in London on April 13th next week. For the weekend reading, check out EigenFold: Generative Protein Structure Prediction with Diffusion Models by Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, and Tommi Jaakkola. The take on protein tasks by the authors of DiffDock 😉