Generative Modelling

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
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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
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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
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gRNAde: Geometric Deep Learning for 3D RNA Inverse Design