Peptidomimetic macrocycles have the potential to regulate challenging therapeutic targets. Structures of this type having precise shapes and drug-like character are particularly coveted, but are relatively difficult to synthesize. Our laboratory has developed robust methods that integrate small-peptide units into designed scaffolds. These methods create macrocycles and embed condensed heterocycles to diversify outcomes and improve pharmacological properties. The hypothetical scope of the methodology is vast and far outpaces the capacity of our experimental format. We now describe a computational rendering of our methodology that creates an in silico three-dimensional library of composite peptidic macrocycles. Our open-source platform, CPMG (Composite Peptide Macrocycle Generator), has algorithmically generated a library of 2,020,794,198 macrocycles that can result from the multistep reaction sequences we have developed. Structures are generated based on predicted site reactivity and filtered on the basis of physical and three-dimensional properties to identify maximally diverse compounds for prioritization. For conformational analyses, we also introduce ConfBuster++, an RDKit port of the open-source software ConfBuster, which allows facile integration with CPMG and ready parallelization for better scalability. Our approach deeply probes ligand space accessible via our synthetic methodology and provides a resource for large-scale virtual screening.

拟肽大环化合物具有调节具有挑战性的治疗靶点的潜力。这种具有精确形状和药物样特性的结构特别令人垂涎，但合成起来相对困难。我们的实验室开发了强大的方法，将小肽单元整合到设计的支架中。这些方法创建大环化合物并嵌入稠合杂环化合物，以使结果多样化并改善药理学特性。该方法的假设范围非常广泛，远远超出了我们实验形式的能力。我们现在描述了我们的方法的计算渲染，该方法创建了复合肽大环的计算机三维库。我们的开源平台 CPMG（复合肽大环生成器）通过算法生成了一个包含 2,020,794,198 个大环的库，这些库可以由我们开发的多步反应序列产生。结构是根据预测的位点反应性生成的，并根据物理和三维特性进行过滤，以识别最大程度不同的化合物以进行优先排序。对于构象分析，我们还引入了 ConfBuster++，它是开源软件 ConfBuster 的 RDKit 端口，它可以轻松地与 CPMG 集成，并准备好并行化，以实现更好的可扩展性。我们的方法通过我们的合成方法深入探测配体空间，并为大规模虚拟筛选提供资源。