Geometric Deep Learning

gRNAde: Geometric Deep Learning for 3D RNA Inverse Design
gRNAde is a geometric deep learning pipeline for 3D RNA inverse design, analogous to ProteinMPNN. gRNAde outperforms physically-based Rosetta and is significantly faster. (Invited book chapter for Methods in Molecular Biology)
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Pietro Liò
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gRNAde: Geometric Deep Learning for 3D RNA Inverse Design
Evaluating Representation Learning on the Protein Structure Universe
Open-source benchmarking framework for evaluating and scaling up protein structure representation learning methods, comparing protein language models and Geometric GNNs trained on AlphaFoldDB, the largest non-redundant protein structure corpus.
Arian R. Jamasb, Alex Moehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Liò, Tom L. Blundell
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Evaluating Representation Learning on the Protein Structure Universe
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
A ‘Hitchhiker’s Guide’ for getting started with deep neural networks for 3D structural biology & chemistry – we wrote it without the goal of publishing at particular venues – as if we could travel back in time and learn from it if we were starting our PhDs today! 🤗
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein
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A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Multi-State RNA Design with Geometric Multi-Graph Neural Networks
We propose gRNAde, a geometric deep learning pipeline for 3D RNA inverse design from one or more backbone structures. (Invited book chapter for Methods in Molecular Biology)
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Pietro Liò
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Multi-State RNA Design with Geometric Multi-Graph Neural Networks
On the Expressive Power of Geometric Graph Neural Networks
We propose a Geometric Weisfeiler-Leman test to study the expressive power of Geometric Graph Neural Networks and demonstrate the advantages of equivariant GNNs over invariant GNNs. (Full paper at ICML 2023, and presented as an Oral at NeurIPS 2022 Workshop)
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò
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On the Expressive Power of Geometric Graph Neural Networks