Computational modelling of Bicyclic peptides to accelerate anti-infective discovery

Infectious diseases are a major global problem, this is likely to only get worse due to
widespread antibiotic resistance. Resistance reduces the available options for treatment of
bacterial infections and therefore we require new innovative drugs to treat drug-resistant
infections. Many pharmaceutical compound collections are focussed on mammalian
targets and lack compounds with the structural features typical of successful antibiotics.
Here we aim to develop and apply novel computational approaches to design and improve
Bicycle compounds with high anti-microbial efficacy. Bicycles are compounds that are
structurally similar to many of the commonly prescribed and highly effective natural
product cyclic peptide antibiotics. This makes them ideal knowledge-based templates for
the strategic computational-design of next generation antibacterial drugs. With the rapidly
growing threat of antimicrobial resistance, such new anti-infective products are urgently
needed