Markus Wenzel

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14

Zero‐shot mutation effect prediction on protein stability and function using RoseTTAFold

Genome-wide prediction of disease variant effects with a deep protein language model

Protein design and variant prediction using autoregressive generative models

Transformer Neural Networks for Protein Family and Interaction Prediction Tasks

SignalP 6.0 predicts all five types of signal peptides using protein language models

Evolutionary-scale prediction of atomic-level protein structure with a language model

Accurate proteome-wide missense variant effect prediction with AlphaMissense

UDSMProt: universal deep sequence models for protein classification