Thorben Froehlking
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tfroehlking.bsky.social
Thorben Froehlking
@tfroehlking.bsky.social
PostDoc, @gervasiolab.bsky.social UNIGE |
PhD, @bussilab.bsky.social SISSA |
#machinelearning, #RNA, #Proteins, #moleculardynamics matching experiments
Reposted by Thorben Froehlking
New #preprint alert! 🧬 MERGE-RNA: a physics-based model to predict #RNA secondary structure ensembles with chemical probing, lead by Giuseppe Sacco, in collaboration with Guido Sanguinetti and Redmond Smyth's lab arxiv.org/abs/2512.20581
MERGE-RNA: a physics-based model to predict RNA secondary structure ensembles with chemical probing
The function of RNA molecules is deeply related to their secondary structure, which determines which nucleobases are accessible for pairing. Most RNA molecules however function through dynamic and het...
arxiv.org
December 24, 2025 at 9:41 AM
Our latest News & Views article with @gervasiolab.bsky.social published in Nature Computational Science. We discuss machine learning of committor-consistent transition pathways.

Read our piece here: www.nature.com/articles/s43...
And the research article here: www.nature.com/articles/s43...
Learning committor-consistent collective variables - Nature Computational Science
An artificial neural network-based strategy is developed to learn committor-consistent transition pathways, providing insight into rare events in biomolecular systems.
www.nature.com
July 23, 2025 at 7:04 AM
Reposted by Thorben Froehlking
📢 Paper on the RBP Footprint Grand Challenge—launched at RNA2021 — is out in @rnajournal.bsky.social doi.org/10.1261/rna....! A community-driven benchmark of methods for predicting RBP binding sites, with contributions from @tfroehlking.bsky.social, Mattia Bernetti, and @bussigio.bsky.social
Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data
A monthly journal publishing high-quality, peer-reviewed research on all topics related to RNA and its metabolism in all organisms
doi.org
May 27, 2025 at 5:37 AM
Reposted by Thorben Froehlking
Work done by Ivan Gilardoni, with help from @piompons.bsky.social @tfroehlking.bsky.social and @bussigio.bsky.social Code is available on GitHub github.com/bussilab/MDR... . Play with it using `pip install MDRefine` pypi.org/project/MDRe...
GitHub - bussilab/MDRefine
Contribute to bussilab/MDRefine development by creating an account on GitHub.
github.com
May 15, 2025 at 12:55 PM
Reposted by Thorben Froehlking