Adam J. Riesselman

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

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.

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.

This paper introduces a method for rapid genome recoding in Salmonella typhimurium by iteratively recombineering synthetic DNA, achieving the highest number of codon substitutions in a single organism.