Marcin J. Skwark

InstaNovo is a transformer model for de novo peptide sequencing that outperforms existing methods, enabling accurate identification of peptides and discovery of novel sequences and organisms.

The paper introduces the Nucleotide Transformer, a foundation model for genomics, showing it can predict molecular phenotypes from DNA sequences with transfer learning across tasks and organisms.

This paper introduces a large language model for plant genomics that provides state-of-the-art predictions for regulatory annotations and variant prioritization, along with a benchmark dataset for deep learning methods.

This study applies offline reinforcement learning to generate protein binders with improved docking and chemical properties by addressing the computational cost of protein-ligand docking.

A COVID-19 Early Warning System combines structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.

The paper introduces a graph neural network for peptide-MHC structure prediction, achieving accuracy comparable to AlphaFold 2 with significantly fewer parameters.

The paper compares metabolic pathways in three mycobacteria to identify common drug targets and assess their druggability.

PconsC3 improves contact prediction for small protein families, enabling accurate structure modeling for thousands of Pfam domains.