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

First broadly generalizable diffusion model for 3D molecular generation. State-of-the-art results for periodic crystals and non-periodic molecular systems through transfer learning. (Spotlight presentation at ICLR 2025 Workshop)

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

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

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

Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds

We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds to learn local and global shape features. (3DV 2022)

Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin

Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds

Learning TSP Requires Rethinking Generalization

We study zero-shot generalization to large-scale instances in neural network-driven solvers for the Travelling Salesman Problem: what architectures, inductive biases and learning paradigms enable better generalization? (CP 2021)

Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

Learning TSP Requires Rethinking Generalization