Miles W. Gander

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 introduces a framework for evaluating edited cell libraries generated by genome engineering, defining metrics and connecting analysis to phenotyping approaches.