Peptides 101: The Tiny Molecular Sentences Your Body Writes
What a peptide actually is, why order and side chains carry the information, and why 13 residues is a different modelling problem from 300.
Machine Learning for Biology01
Series · 6 parts · 2,025–2,026
Machine Learning for Biology
From what a peptide is, through the descriptor zoo and protein language models, to screening four hundred thousand natural products and docking the survivors.
Turning a Peptide Into Numbers: A Tour of the Descriptor Zoo
AAC, DPC, DDE, CKSAAP, CTD, PAAC and QSO: every way I know to turn a peptide into a fixed-length vector, and what each one throws away.
Machine Learning for Biology02
What ESM-2 Learned That Letter-Counting Couldn't
A 650-million-parameter protein language model, what its embeddings really encode, and why twenty lines of letter counting still beat it on 13-residue peptides.
Machine Learning for Biology03
Your Negatives Decide Your Accuracy: 92% That Means Nothing
Random UniProt negatives quietly turn peptide prediction into short versus long. Here is what an honest benchmark costs, and why it is worth paying.
Machine Learning for Biology04
From 400,000 Natural Products to 20 Candidates
A funnel from every catalogued natural product down to twenty compounds worth testing, and why the split and the base rate matter more than the model.
Machine Learning for Biology05
Molecular Docking Without the Jargon: A Key, a Lock, and Both of Them Wobble
What a docking score really is, why the pose matters more than the number, six ways docking lies to you, and where 2026 co-folding models fit.
Machine Learning for Biology06