Computational modelling of Bicyclic peptides to accelerate anti-infective discovery

传染病是一个主要的全球性问题，由于抗生素耐药性的传播，这一问题可能只会变得更糟。耐药性减少了可用于治疗细菌感染的选择，因此我们需要新的创新药物来治疗耐药性感染。许多药物化合物收藏集中在哺乳动物的靶标上，缺乏具有成功抗生素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自行车化合物。自行车是一种化合物，其结构类似于许多常用的处方和高效的天然产品环肽抗生素。这使得它们成为下一代抗菌药物战略计算设计的理想基于知识的模板。随着抗菌素耐药性威胁的迅速增长，迫切需要这样的新型抗感染产品

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