RNA

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
Getting started with 3D RNA modelling as a machine learner
A currated reading + watch list for beginners to get started with RNA modelling and desing.
Chaitanya K. Joshi
May 1, 2024 7 min read
Related Paper Curated playlist Tutorial
Getting started with 3D RNA modelling as a machine learner
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
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gRNAde: Geometric Deep Learning for 3D RNA Inverse Design
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