De novo prediction of RNA 3D structures with deep generative models.
We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a score model to find and rank the most likely folding structures for a given RNA sequence. We sh...
Main Authors: | Julius Ramakers, Christopher Frederik Blum, Sabrina König, Stefan Harmeling, Markus Kollmann |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2024-01-01
|
Series: | PLoS ONE |
Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297105&type=printable |
Similar Items
-
De novo prediction of RNA 3D structures with deep generative models
by: Julius Ramakers, et al.
Published: (2024-01-01) -
Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.
by: Xiangyun Qiu
Published: (2023-04-01) -
Liquified protein vibrations, classification and cross-paradigm de novo image generation using deep neural networks
by: Buehler, Markus J
Published: (2021) -
Caveats to Deep Learning Approaches to RNA Secondary Structure Prediction
by: Christoph Flamm , et al.
Published: (2022-07-01) -
Generative discovery of de novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents
by: Luu, Rachel K, et al.
Published: (2024)