Molecular scaffold analysis of natural products databases in the public domain.

Natural products represent important sources of bioactive compounds in drug discovery efforts. In this work, we compiled five natural products databases available in the public domain and performed a comprehensive chemoinformatic analysis focused on the content and diversity of the scaffolds with an overview of the diversity based on molecular fingerprints. The natural products databases were compared with each other and with a set of molecules obtained from in-house combinatorial libraries, and with a general screening commercial library. It was found that publicly available natural products databases have different scaffold diversity. In contrast to the common concept that larger libraries have the largest scaffold diversity, the largest natural products collection analyzed in this work was not the most diverse. The general screening library showed, overall, the highest scaffold diversity. However, considering the most frequent scaffolds, the general reference library was the least diverse. In general, natural products databases in the public domain showed low molecule overlap. In addition to benzene and acyclic compounds, flavones, coumarins, and flavanones were identified as the most frequent molecular scaffolds across the different natural products collections. The results of this work have direct implications in the computational and experimental screening of natural product databases for drug discovery.

Bioactivity landscape modeling: chemoinformatic characterization of structure-activity relationships of compounds tested across multiple targets.

Characterizing structure-activity relationships (SAR) of sets of compounds screened across different targets is crucial in several drug discovery endeavors. To this end, chemoinformatic approaches are emerging to characterize SARs using the concept of multi-target activity landscapes. Herein, we present the Structure multiple Activity Similarity (SmAS) maps and the Structure multiple Activity Landscape Index (SmALI) as general approaches to navigate through and quantify the most informative regions of multi-target activity landscapes. These methods are extensions of SAS maps and SALI metric used for single targets. To illustrate the use of these methods, SmAS maps and SmALI values were employed for characterizing the SAR of three benchmark sets of compounds screened with different target families. As a follow up of our work, we employed four 2D and 3D structure representations to obtain consensus models for each data set. For the three data sets, we identified pairs of compounds with high structure similarity but very different bioactivity profile across the corresponding targets of each family that is, multi-target activity cliffs. Also, we identified pairs of compounds with low structure similarity but similar bioactivity profile across the different targets that is, multi-target scaffold hops. The consensus SmAS maps and mean SmALI metric are complementary chemoinformatic tools to systematically describe multi-target activity landscapes.