Computational modelling of Bicyclic peptides to accelerate anti-infective discovery

传染病是一个主要的全球性问题，由于广泛的抗生素耐药性，这可能只会变得更糟。耐药性减少了治疗细菌感染的可用选择，因此我们需要新的创新药物来治疗耐药性感染。许多药物化合物集合集中于哺乳动物靶标，并且缺乏具有成功抗生素的典型结构特征的化合物。在这里，我们的目标是开发和应用新的计算方法来设计和改进具有高抗微生物功效的双环化合物。双环是结构上类似于许多常用处方和高效天然产物环肽抗生素的化合物。这使得它们成为下一代抗菌药物战略计算设计的理想知识模板。随着抗菌素耐药性威胁的迅速增长，迫切需要此类新型抗感染产品

Infectious diseases are a major global problem, this is likely to only get worse due towidespread 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