Goran Rakocevic

Unlocking de novo antibody design with generative artificial intelligence

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.

Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness

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.

Fast and Accurate Genomic Analyses using Genome Graphs

This paper introduces a graph genome reference that improves read alignment and variant calling accuracy compared to traditional haplotype-based methods.

Comparing complex variants in family trios

This paper introduces VBT, a tool for identifying Mendelian violations in family trios by solving the 3-way variant matching problem, showing improved accuracy over existing methods.

Genome Graphs

This paper introduces genome graphs as an alternative to linear reference genomes, showing they improve read mapping and variant calling compared to the standard GRCh38 reference.