Generative Modelling

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. (ICML 2025 & Spotlight 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

gRNAde: Geometric Deep Learning for 3D RNA Inverse Design

A new generative toolkit for inverse design of 3D RNA structure. gRNAde has been validated in wet labs, achieving expert-level accuracy and outperforming all previous computation methods at designing RNA structure and function. (ICLR 2025 Spotlight & 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