John M. Sutton

Origin-1 generates high-confidence, specific antibodies against zero-prior epitopes through generative AI, validated with biophysical assays and cryo-EM.

IgDesign designs antibody CDRs using deep learning and validates them in vitro, showing high success rates and improved affinities for multiple therapeutic antigens.

The paper develops deep learning models trained on synonymous variant datasets to predict and optimize protein expression in E. coli.

Deep generative models combined with high-throughput screening design de novo anti-HER2 antibodies without further optimization, yielding nanomolar binders functionally comparable or superior to trastuzumab.

Language models trained on high-throughput affinity data predict KD of unseen antibody variants across three orders of magnitude, and a "naturalness" score can be optimised alongside affinity.

The paper evaluates optimal experimental design for genome sequencing and assembly using Oxford Nanopore Technologies by simulating and analyzing data from multiple organisms.