Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Abstract

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis–Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets

“…Generating automatically and consistently the main scaffold or core structure of large data sets can be done in several ways as recently reviewed [24] . In general, it is desirable to generate the scaffolds rapidly, consistently, and interpretable, in particular for an organic or medicinal chemist working on chemical synthesis.…”

“…[14] They are helpful at concisely depicting the structure-activity (property) relationships -SA(P)R -in a summarized representation of the data set, as analog series can be represented in fewer data points than individual compounds. [23,24] Of note, only a fraction of the total data is presented in the constellation plot: the compounds forming analog series; in this case, we included only analog series consisting of at least three compounds. Recently, constellation plots have been used to describe a library of antidiabetic natural products [25] and a collection of tubulin inhibitors.…”

“…Constellation plots are useful to depict the chemical space of chemical libraries containing several analog series [14] . They are helpful at concisely depicting the structure‐activity (property) relationships – SA(P)R – in a summarized representation of the data set, as analog series can be represented in fewer data points than individual compounds [23,24] . Of note, only a fraction of the total data is presented in the constellation plot: the compounds forming analog series; in this case, we included only analog series consisting of at least three compounds.…”

“…Generating automatically and consistently the main scaffold or core structure of large data sets can be done in several ways as recently reviewed. [24] In general, it is desirable to generate the scaffolds rapidly, consistently, and interpretable, in particular for an organic or medicinal chemist working on chemical synthesis. As detailed in the Methods section, in this work we implemented two methodologies generating the Bemis-Murcko scaffolds and analog series based on core scaffolds (Figure 2).…”

Scaffold Generator - A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)

“…This way, the scaffolds extracted as representatives of an analog series take synthetic accessibility into account, an important aspect in medicinal chemistry but mostly ignored in the approaches above. The analog series and their representative scaffolds can be visualised by R-group tables, mapped into coordinate-based chemical space [32], annotated with activity information to support SAR studies, or used to extract favourable lead structures for drug design campaigns [17,18,[33][34][35][36][37][38].…”