Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Curr Opin Chem Biol. 2018 Jun;44:87-92. doi: 10.1016/j.cbpa.2018.06.006. Epub 2018 Jun 17.
The new generation of post-genomic targets, such as protein-protein interactions (PPIs), often require new chemotypes not well represented in current compound libraries. This is one reason for why traditional high throughput screening (HTS) approaches are not more successful in delivering medicinal chemistry starting points for PPIs. In silico screening methods of an expanded chemical space are then potential alternatives for developing novel chemical probes to modulate PPIs. In this review, we report on the state-of-the-art pipelines for virtual screening, emphasizing prospectively validated methods capable of addressing the challenge of drugging difficult targets in the human interactome. Collectively, we show that optimal strategies for structure based virtual screening vary depending on receptor structure and degree of flexibility.
新一代的后基因组靶点,如蛋白质-蛋白质相互作用(PPIs),通常需要新型的化学型,而这些化学型在当前的化合物库中没有很好地体现。这就是为什么传统的高通量筛选(HTS)方法在提供 PPI 的药物化学起点方面并不那么成功的原因之一。那么,扩展化学空间的计算筛选方法是开发新型化学探针来调节 PPI 的潜在替代方法。在这篇综述中,我们报告了虚拟筛选的最新技术管道,重点是前瞻性验证的方法,这些方法能够解决在人类相互作用组中对难靶标进行药物治疗的挑战。总的来说,我们表明,基于结构的虚拟筛选的最佳策略取决于受体结构和灵活性程度。