An alarming rise in pathogens that show antibiotic resistance has been observed over recent years. In the case of Gram-negative bacterial pathogens, the resistance crisis has started to go out of control. Due to the lower permeability of the Gram-negative cell envelope for antibiotics, these pathogens are inherently more difficult to treat. The lower cell penetration of new drug candidates is also reflected in the failure of medicinal chemistry to advance novel classes of compounds with Gram-negative activity. This substantially increases the costs required for Gram-negative drug discovery. Therefore, the development of therapeutics against Gram-negative infections and their treatment faces a number of key bottlenecks, most of which are related to poor drug uptake.
The structural and chemical properties of antibiotics that determine if it can, or cannot, overcome the cell wall barrier are still insufficiently understood. We hypothesise that the use of artificial intelligence will be instrumental in helping to distinguish drug candidates that will be taken up by the bacteria from those that will be held back or expelled from the pathogens through efflux pumps. We will work on data sets containing information on compound permeation into microbes, chemical structure, and physical-chemical properties. The available data sets will be expanded by recording our own data on antibiotic efficiency in collaboration with Orbital Diagnostics Ltd., St. Andrews, using the novel SLIC technique. We will then develop and make use of machine-learning approaches to analyse these data to find out which properties turn a chemical compound into a well-permeating drug candidate, and which properties may help in avoiding efflux. The outcome of this work is anticipated to have a crucial role in the search for new and better antibiotics.