Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.