Quantifying structure and performance diversity for sets of small molecules comprising small-molecule screening collections

Abstract

Using a diverse collection of small molecules we recently found that compound sets from different sources (commercial; academic; natural) have different protein-binding behaviors, and these behaviors correlate with trends in stereochemical complexity for these compound sets. These results lend insight into structural features that synthetic chemists might target when synthesizing screening collections for biological discovery. We report extensive characterization of structural properties and diversity of biological performance for these compounds and expand comparative analyses to include physicochemical properties and three-dimensional shapes of predicted conformers. The results highlight additional similarities and differences between the sets, but also the dependence of such comparisons on the choice of molecular descriptors. Using a protein-binding dataset, we introduce an information-theoretic measure to assess diversity of performance with a constraint on specificity. Rather than relying on finding individual active compounds, this measure allows rational judgment of compound subsets as groups. We also apply this measure to publicly available data from ChemBank for the same compound sets across a diverse group of functional assays. We find that performance diversity of compound sets is relatively stable across a range of property values as judged by this measure, both in protein-binding studies and functional assays. Because building screening collections with improved performance depends on efficient use of synthetic organic chemistry resources, these studies illustrate an important quantitative framework to help prioritize choices made in building such collections.A central theme in applying cheminformatics to discovery chemistry is to relate synthetic decisions to consequences on both chemical structure and biological assay performance. Historically, such efforts focused on small sets of similar compounds, and single performance measurements (1-3), providing guidance to chemists in compound optimization against singletarget proteins or processes (4). However, additional methods are needed to judge large sets of compounds, such as those used in small-molecule screening. Progress toward more valuable screening collections (5) requires unbiased methods to evaluate diversity of assay performance for compound sets rather than performance of individual members.A widely used method to judge compounds for drug discovery is the "rule of 5" (RO5) (6), which predicts poor absorption or permeation for compounds that deviate from property-value constraints: H-bond donors (Hd) and acceptors (Ha), molecular weight (MW), and calculated partition coefficients (cLogP). Recent studies have attempted to refine such rules (7-9) and extend them to other goals (10-13), such as making leads or probes. Such property filters have been debated and reviewed (14-16), and their long-term impact on pharmaceutical research is starting to be analyzed (17, 18). Importantly, exceptions to these rules, including natural products (19-21), ...

Expansion of chemical space for collaborative lead generation and drug discovery: the European Lead Factory Perspective

“…These compounds are compared to the Maybridge Screening Collection (http://www.maybridge.com/portal/alias__Rainbow/lang__en/tabID__146/DesktopDefault.aspx), the collection of the Molecular Library Program (MLP) of the National Institutes of Health (NIH) [11], and compounds curated in the Page 3 of 11 A c c e p t e d M a n u s c r i p t ChEMBL database [12]. The Maybridge collection was chosen as a representative screening library from commercial sources because of its diversity-based character and previous use in screening collections comparisons [13]. The MLP library was selected because of the similar collaborative nature of the MLP and ELF programs, and the ChEMBL database because of the medicinal chemistry relevance of the compounds curated therein.…”

Molecular Scaffold Analysis of Natural Products Databases in the Public Domain

“…To generate the CSR curves, the scaffolds are ordered by their frequency of occurrence (most to least common). Then, the fraction of scaffolds is plotted on the x-axis and the fraction of compounds that contain those scaffolds on the The structures were retrieved from Clemons et al [19].…”

“…Several chemoinformatic analyses of natural products databases have been published. Examples of recent analysis include a comparison of the molecular complexities and structural diversities of three databases, including a natural product collection, and how these properties were related to specificity and diversity of biological performance (19). The authors demonstrated that compound specificity was associated with molecular complexity.…”

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling

“…This approach aimed at guiding the creation of effective screening collections for cell-based, phenotypic HTS. We also applied parallel biochemical assay profiling (15) to explore relationships between protein-binding performance diversity and similar chemical features as well as the role of origins of compounds. The latter study addresses the problem of defining effective screening collections for biochemistry-based HTS involving protein binding and activity modulation (for example, enzyme inhibition).…”