Simon V. Mathis

This paper introduces gRNAde, an RNA language model for inverse design of RNA structure and function, achieving success rates comparable to human experts in designing pseudoknotted RNAs.

An all-atom diffusion model using a 14-atom residue representation and sparse U-Net architecture accelerates biomolecular design 10-fold, enabling experimentally validated DNA-binding proteins and multi-turnover cysteine hydrolases.

AtomWorks, a modular atom-level data framework, trains structure prediction and design models; the resulting RF3 improves chirality handling and performs between AF3 and Boltz.

The paper combines microfluidic screening with machine learning to rapidly engineer biocatalysts, achieving 23-fold improvement in catalytic efficiency through sequence-function mapping.

gRNAde designs RNA sequences based on 3D backbone structures using geometric deep learning, achieving higher native sequence recovery than Rosetta on benchmark tasks.