前列腺癌（PCa）是男性主要癌症之一，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 (MP) 在 PCa 的发生和进展中发挥着至关重要的作用，并且可以成为有吸引力的治疗靶点。然而，针对 MP 的实验限制阻碍了有效的生物标志物和抑制剂的发现。为了克服这一障碍，计算方法可以产生结构见解并筛选大型化合物库，从而加速先导化合物的识别和优化。在这篇综述中，我们研究了当前计算机辅助药物设计 (CADD) 方面的突破，重点是针对最相关的膜结合 PCa 生物标志物的基于结构的方法。版权所有 © 2024 作者。由爱思唯尔有限公司出版。保留所有权利。

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.Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.