Aaron W. Kollasch

The paper develops a synthetic Fab yeast display library integrated with machine learning to discover antibodies against cell surface antigens, yielding hundreds of validated antibodies and a public dataset.

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

This paper uses machine learning to predict high-affinity nanobody binders from sparse sequencing data, outperforming deep learning and semi-supervised methods with linear models.

This paper introduces popEVE, a deep generative model for proteome-wide disease variant prediction, identifying 442 genes in a developmental disorder cohort, including 123 novel candidates.

RNAGym provides a benchmarking framework for RNA fitness, secondary structure, and tertiary structure prediction using diverse datasets and standardized assays.

The paper introduces BaseData, a new biological sequence database that expands known protein diversity by over 10x using a global biodiscovery pipeline.

ProteinGym unifies over 250 deep mutational scans and ~65k clinical variants to benchmark over 70 models in zero-shot and supervised settings; TranceptEVE ranks best overall.

Blending an alignment-based family model with an autoregressive protein language model, weighted by how deep the alignment is, reportedly improves mutation-effect prediction against experimental assays and ClinVar.

This paper introduces an alignment-free deep generative model for protein design and variant effect prediction, showing improved performance over existing methods in challenging cases.