Abstract：
Prostate cancer (PCa) is one of the leading cancers in men and the lack of suitable biomarkers or their modulators results in poor prognosis. Membrane proteins (MPs) have a crucial role in the development and progression of PCa and can be attractive therapeutic targets. However, experimental limitations in targeting MPs hinder effective biomarker and inhibitor discovery. To overcome this barrier, computational methods can yield structural insights and screen large libraries of compounds, accelerating lead identification and optimization. In this review, we examine current breakthroughs in computer-aided drug design (CADD), with emphasis on structure-based approaches targeting the most relevant membrane-bound PCa biomarkers.
摘要：
前列腺癌（PCa）是男性的主要癌症之一，缺乏合适的生物标志物或其调节剂会导致预后不良。膜蛋白（MPs）在PCa的发生和发展中起着至关重要的作用，并且可以成为有吸引力的治疗靶标。然而,靶向MPs的实验局限性阻碍了有效的生物标志物和抑制剂的发现。为了克服这个障碍，计算方法可以产生结构洞察和筛选大型化合物库，加速线索识别和优化。在这次审查中,我们研究了计算机辅助药物设计（CADD）的当前突破，重点是靶向最相关的膜结合PCa生物标志物的基于结构的方法。
