Geometric Deep Learning

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials

First broadly generalization diffusion model for 3D molecular generation. State-of-the-art results for periodic crystals and non-periodic molecular systems through transfer learning.

Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design

RNA-FrameFlow is a generative model for 3D RNA backbone design based on SE(3) flow matching. We evaluated the extent to which protein generative models can be adapted for RNA.

Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Liò

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design

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 V. Mathis, Alex Morehead, Rishabh Anand, Pietro Liò

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

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

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

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. (Oral presentation at NeurIPS 2022 Workshop)

Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò

On the Expressive Power of Geometric Graph Neural Networks