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Scaffolds were originally introduced to delineate core structures of active compounds. They are also used to assess the ability of computational methods to identify structurally diverse active compounds. Biological activities of compound series are often mapped to scaffolds. This is done to better understand activity distributions over different structural classes or search for core structures of compounds that are preferentially active against target families of interest. Herein, we describe in detail how such scaffold activity profiles are generated and compare profiles for differently defined scaffolds. As an exemplary application, scaffolds of currently available kinase inhibitors covering the human kinome are analyzed.

Publicly available kinase inhibitors provide a large source of information for structure-activity relationship analysis and kinase drug design. In this study, publicly available inhibitors of the human kinome were collected and analog series formed by kinase inhibitors systematically identified. Then, alternative scaffold concepts were applied to assess diversity and promiscuity of kinase inhibitors. Over the past two years, the number of publicly available kinase inhibitors with high-confidence activity data more than doubled, but coverage of the human kinome only slightly increased. Approximately 70% of current kinase inhibitors belonged to analog series. However, the detectable degree of promiscuity among these kinase inhibitors remained low. Approximately 76% of all inhibitors were only annotated with a single kinase, compared to ~70% two years ago. For many kinases, the assessment of scaffold diversity among their inhibitors and the distribution of differently defined scaffolds over analog series made it possible to assess scaffold hopping potential. Our analysis revealed that the consideration of conventional compound-based scaffolds most likely leads to an overestimation of scaffold hopping frequency, at least for compounds forming analog series.

A computational methodology is introduced for detecting all unique series of analogs in large compound data sets, regardless of chemical relationships between analogs. No prior knowledge of core structures or R-groups is required, which are automatically determined. The approach is based upon the generation of retrosynthetic matched molecular pairs and analog networks from which distinct series are isolated. The methodology was applied to systematically extract more than 17 000 distinct series from the ChEMBL database. For comparison, analog series were also isolated from screening compounds and drugs. Known biological activities were mapped to series from ChEMBL, and in more than 13 000 of these series, key compounds were identified that represented substitution sites of all analogs within a series and its complete activity profile. The analog series, key compounds, and activity profiles are made freely available as a resource for medicinal chemistry applications.

From currently available kinase inhibitors and their activity data, clusters of coordinated activity cliffs were systematically derived and subjected to cluster index and index map analysis. Type I-like inhibitors with well-defined IC50 measurements were found to provide a large knowledge base of activity cliff clusters for 266 targets from nine kinase groups. On the basis of index map analysis, these clusters were systematically organized according to structural similarity of inhibitors and activity cliff diversity and prioritized for structure-activity relationship (SAR) analysis. From prioritized clusters, interpretable SAR information can be extracted. It is also shown that activity cliff clusters formed by ATP site-directed inhibitors often represent local SAR environments of rather different complexity and interpretability. In addition, activity cliff clusters including promiscuous kinase inhibitors have been determined. Only a small subset of inhibitors was found to change activity cliff roles in different clusters. The activity cliff clusters described herein and their index map organization substantially enrich SAR information associated with kinase inhibitors in compound subsets of limited size. The cluster and index map information is made available upon request to provide opportunities for further SAR exploration. On the basis of our analysis and the data provided, activity cliff clusters and corresponding inhibitor series for kinase targets of interest can be readily selected., (Copyright © 2014 Elsevier Masson SAS. All rights reserved.)

A library of 484 imidazole-based candidate inhibitors was tested against 24 protein kinases. The resulting activity data have been systematically analyzed to search for compounds that effectively differentiate between kinases. Six imidazole derivatives with high kinase differentiation potential were identified. Nearest neighbor analysis revealed the presence of close analogues with varying differentiation potential. Small structural modifications of active compounds were found to shift their inhibitory profiles toward kinases with different functions.