Use of Arti?cial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad Spectrum of Highly Antibiotic-Resistant Superbugs
ABSTRACT Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society’s most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by
capitalizing on accumulating chemical biology information. Using peptide array
technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant
data was used together with Arti?cial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the
basis of random testing, these models proved remarkably effective in predicting
the activity of 100,000 virtual peptides. The best peptides, representing the top
quartile of predicted activities, were effective against a broad array of multidrugresistant “Superbugs” with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models.