Do you want to work at the interface of structural biology and #AI? We are #hiring Come to Paris for a CNRS 🌍 postdoc at Institut Pasteur, since 1887 funded by European Research Council (ERC) and co-supervised by @malletvincent Please RT!! #deeplearning #compbio More info here: research.pasteur.fr/b/vVT

AtomSurf: Surface Representation for Learning on Protein Structures 1/ AtomSurf introduces a novel surface representation approach for protein structure learning, significantly advancing previous methodologies by integrating surface and graph-based encoders for enhanced

10 days left to apply for this postdoctoral position CNRS 🌍 Institut Pasteur, since 1887 European Research Council (ERC) 😱

RNAmigos2: Fast and accurate structure-based RNA virtual screening with semi-supervised graph learning and large-scale docking data 1. RNAmigos2 changes RNA-targeted drug discovery with a machine-learning pipeline that outpaces traditional docking by running over 10,000 times

Join my team! Please, tweet it. #AI #MachineLearningandAI #Artificialintelligence #MachineLearning #proteins #enzymes

Our latest efforts in AI-driven RNA drug discovery (RNAmigos2) have been published in Nature Communications. Blessed to have worked with such a talented team: Vincent Mallet, J. Waldispühl, JG Patiño, et al. Paper: nature.com/articles/s4146… GitHub: github.com/cgoliver/rnami… Powered

A Comprehensive Benchmark for RNA 3D Structure-Function Modeling 1. This study introduces a comprehensive benchmarking suite for RNA structure-function modeling, addressing a significant gap in the field by providing datasets for various RNA-related tasks. 2. Seven RNA

If you, like me, felt frustrated after reviewing 6 papers for NeurIPS Conference and thought "there must be a better way" then this post is for you. Link 👇

Our Ambient Protein Diffusion code is now available. We trained a structure generation model on AlphaFoldDB that produces designable long proteins. AlphaFoldDB contains proteins of varying quality. Our approach explicitly accounts for this during diffusion training.

Leveraging Protein Representations to Explore Uncharted Fold Spaces with Generative Models 1. This novel study introduces DiffTopo, a novel coarse-grained protein structure representation method that uses diffusion models to efficiently explore uncharted areas of the protein

It was an honor to contribute a small part to this expansive and highly insightful look at the current state of ML-assisted RNA drug design led by Vincent Mallet and Wissam Karroucha. Most exciting is a new benchmark on virtual screening specificity (including Boltz2).

Learning Dynamic Protein Representations at Scale with Distograms 1. The authors bypass expensive MD by mining AlphaFold2/Boltz2 distograms—probability maps of residue–residue distances—to inject true conformational heterogeneity into graph neural networks without generating a