Hunting FOX: Using Fragments to Sniff Out Drug Leads for Antibiotic Discovery

Drug discovery faces a potential crisis. Due a dwindling "obvious" search space, productivity is declining. This has led to the idea of "drug repurposing," in which data on drugs that have failed clinical trials are reused. We approach drug repurposing by introducing an algorithm–which we term "Hunting FOX" for "Hunting Fragments Of X"–that combines a fragment-based representation with traditional machine learning to identify the most important submolecules correlating with an activity of interest. We validate our approach on the problem of drug permeation through the highly-impermeable outer membrane of P. aeruginosa. We show that Hunting FOX is able to recapitulate a set of relevant fragments with understandable physicochemical properties. By using coarse-grained molecular dynamics, we show a possible mechanism behind fragment-based permeation enhancement. We also fit a predictive classifier using the identified fragments and verify its predictions experimentally. Overall, we have developed a novel algorithm of great use in improving outcomes for drug discovery and validated it as applied to the design of antibiotics that can permeate Gram-negative bacteria.