Using machine learning to design peptides

(Northwestern University) Northwestern, teaming up with Cornell University and the University of California, San Diego, developed a way of finding optimal peptide sequences: using a machine-learning algorithm as a collaborator. The algorithm analyzes experimental data and offers suggestions on the next best sequence to try, creating a back-and-forth selection process that reduces time needed to find the optimal peptide.The results, published in Nature Communications, could provide a new framework for experiments across materials science and chemistry.