The success of ligand-based virtual-screening calculations is influenced highly by the nature of target-specific structure-activity relationships. This might pose severe constraints on the ability to recognize diverse structures with similar activity. Accordingly, the performance of similarity-based methods strongly depends on the class of compound that is studied, and approaches of different design and complexity often produce, overall, equally good (or bad) results. However, it is also found that there is often little overlap in the similarity relationships detected by different approaches, which rationalizes the need to develop alternative similarity methods. Among others, these include novel algorithms to navigate high-dimensional chemical spaces, train similarity calculations on specific compound classes, and detect remote similarity relationships.

中文翻译：

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虚拟筛选中的分子相似性分析：基础，局限性和新方法。

基于配体的虚拟筛选计算的成功很大程度上受靶标特异性结构-活性关系的影响。这可能会严重限制识别具有相似活动的不同结构的能力。因此，基于相似度的方法的性能在很大程度上取决于所研究化合物的种类，并且不同设计和复杂性的方法通常会产生总体上相同（或不好）的结果。但是，还发现通过不同方法检测到的相似性关系之间几乎没有重叠，这合理化了开发替代相似性方法的必要性。其中包括新颖的算法，可在高维化学空间中导航，针对特定化合物类别训练相似度计算，