Alex Rogozhnikov

This paper introduces a computational antibody design platform that generates drug-like antibodies with high developability and atomic accuracy, demonstrating its utility for challenging therapeutic targets.

This paper introduces Chai-2, a model that designs antibodies with a 16% hit rate, achieving success in 50% of targets without prior binders, and enables rapid validation in under two weeks.

Chai-1 matches AlphaFold3 on PoseBusters ligand poses (77% vs 76%), beats AlphaFold-Multimer 2.3 on low-homology interfaces, and stays competitive without MSAs on multimers and antibodies.

This paper introduces a non-parametric method to detect hierarchical confounders in biological datasets and demonstrates its application in biomedical image and multi-phenotypic data.

Demuxalot improves genetic demultiplexing for single-cell RNA sequencing by enhancing accuracy and efficiency in multi-batch, multi-donor studies.

This paper develops an automated system to scale patient-derived brain organoids, revealing consistent donor and batch effects and demonstrating reproducible disease phenotypes in tuberous sclerosis models.