How Diverse Are Diversity Assessment Methods? A Comparative Analysis and Benchmarking of Molecular Descriptor Space

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, “Bayes Affinity Fingerprints”). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that ”Bayes Affinity Fingerprints” achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.化学多样性（chemical diversity）是一种被广泛应用的方法，用于筛选结构多样化的分子子集，其核心目标通常是最大化生物筛选中的命中化合物数量。尽管该领域已存在诸多方法，但针对当前常用描述符开展系统性比较的研究仍较为匮乏，尤其是以评估生物活性空间多样性为目标的相关成果，本研究正是旨在填补这一空白。 本研究共对比了13种常用分子描述符（molecular descriptors），包括基于指纹的描述符（ECFP4、FCFP4、MACCS keys）、基于药效团的描述符（TAT、TAD、TGT、TGD、GpiDAPH3）、基于形状的描述符（快速化学结构叠合（ROCS，rapid overlay of chemical structures）以及惯性主矩（PMI，principal moments of inertia））、基于连接性矩阵的描述符（BCUT）、基于理化性质的描述符（prop2D），以及近年提出的新型分子描述符类型——“贝叶斯亲和指纹（Bayes Affinity Fingerprints）”。 我们同时评估了两类性能：一是各描述符在评估化学库多样性时的相似表现，二是其从分子库中筛选出生物活性空间多样化化合物的能力——这一属性在筛选库设计中具有极高的实际应用价值。鉴于诸多待筛选的未来靶点尚未提前确定，但筛选库仍需最大化覆盖未来筛选项目中活性物质的检出可能性，这一点显得尤为关键。 总体而言，研究结果表明，基于原子拓扑结构的描述符（即基于指纹的描述符与基于药效团的描述符）在化合物排序上具有较强的相关性，且在同类描述符内部与不同描述符之间均呈现出一致的表现。与之相反，诸如ROCS与PMI这类基于形状的描述符与本研究中使用的其他描述符相关性较弱，展现出显著不同的行为模式。 随后，我们从包含2587种化合物的初始库中（覆盖ChEMBL数据库中25个最大的人类活性类别），应用本研究对比的8种分子描述符进行采样，选取占比4%的多样化化合物子集，并评估了各子集对活性类别的覆盖度。结果发现，“贝叶斯亲和指纹”的平均活性类别覆盖度达到92%。使用ECFP4、GpiDAPH3、TGT以及随机采样方法时，所选化合物对应的活性类别占比分别为91%、84%、84%与84%，紧随其后的依次为BCUT、prop2D、MACCS与PMI（按性能从高到低排序）。 此外，我们还证实，尽管PMI图常被用于评估生物活性空间多样性，但化合物在PMI空间的多样性与生物活性空间的多样性之间并无显著相关性。 综上，本研究评估了哪些分子描述符能够筛选出高覆盖生物活性空间的化合物，因此可用于为生物筛选选择多样化的化合物子集。当需要使用多种描述符进行多样性筛选时，本研究明确了哪些描述符具有互补性，可联合使用以聚焦化学空间中多样性的不同维度。