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

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.

Systematic search for benzimidazole compounds and derivatives with antileishmanial effects.

Leishmaniasis is a neglected tropical disease that currently affects 12 million people, and over 1 billion people are at risk of infection. Current chemotherapeutic approaches used to treat this disease are unsatisfactory, and the limitations of these drugs highlight the necessity to develop treatments with improved efficacy and safety. To inform the rational design and development of more efficient therapies, the present study reports a chemoinformatic approach using the ChEMBL database to retrieve benzimidazole as a target scaffold. Our analysis revealed that a limited number of studies had investigated the antileishmanial effects of benzimidazoles. Among this limited number, L. major was the species most commonly used to evaluate the antileishmanial effects of these compounds, whereas L. amazonensis and L. braziliensis were used least often in the reported studies. The antileishmanial activities of benzimidazole derivatives were notably variable, a fact that may depend on the substitution pattern of the scaffold. In addition, we investigated the effects of a benzimidazole derivative on promastigotes and amastigotes of L. infantum and L. amazonensis using a novel fluorometric method. Significant antileishmanial effects were observed on both species, with L. amazonensis being the most sensitive. To the best of our knowledge, this chemoinformatic analysis represents the first attempt to determine the relevance of benzimidazole scaffolds for antileishmanial drug discovery using the ChEMBL database. The present findings will provide relevant information for future structure-activity relationship studies and for the investigation of benzimidazole-derived drugs as potential treatments for leishmaniasis.

Dark matter RNA: an intelligent scaffold for the dynamic regulation of the nuclear information landscape.

Perhaps no other topic in contemporary genomics has inspired such diverse viewpoints as the 95+% of the genome, previously known as "junk DNA," that does not code for proteins. Here, we present a theory in which dark matter RNA plays a role in the generation of a landscape of spatial micro-domains coupled to the information signaling matrix of the nuclear landscape. Within and between these micro-domains, dark matter RNAs additionally function to tether RNA interacting proteins and complexes of many different types, and by doing so, allow for a higher performance of the various processes requiring them at ultra-fast rates. This improves signal to noise characteristics of RNA processing, trafficking, and epigenetic signaling, where competition and differential RNA binding among proteins drives the computational decisions inherent in regulatory events.