John Ingraham

This study uses evolutionary models to design highly divergent protein variants with enhanced functional properties while maintaining structure.

This paper introduces Chroma, a generative model for proteins that can sample novel structures and sequences, conditioned on desired properties and functions.

The paper introduces a hybrid machine learning approach for perturbation biology to identify combinatorial therapeutic candidates using a melanoma cell line dataset.

The paper introduces EVcouplings, a Python framework for coevolutionary sequence analysis that enables structure prediction and mutation effect assessment.

This paper introduces DeepSequence, a deep generative model that captures mutation effects in biomolecules by modeling higher-order dependencies beyond pairwise interactions.