Writing on machine learning in genomics, proteins, and drug discovery.

Sequence models, variant-effect prediction, and the work to read non-coding DNA.

Single-sequence folding, protein language models, and structure without alignment.

Contrastive screening, protein–ligand interaction, and design that survives the wet lab.

What models encode, where they fail, and how evaluation and oversight hold up.