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Silychristin is the second most abundant flavonolignan (after silybin) present in the fruits of Silybum marianum. A group of compounds containing silychristin (3) and its derivatives such as 2,3-dehydrosilychristin (4), 2,3-dehydroanhydrosilychristin (5), anhydrosilychristin (6), silyhermin (7), and isosilychristin (8) were studied. Physicochemical data of these compounds acquired at high resolution were compared. The absolute configuration of silyhermin (7) was proposed to be identical to silychristin A (3a) in ring D (10R,11S). The preparation of 2,3-dehydrosilychristin (4) was optimized. The Folin-Ciocalteau reduction and DPPH and ABTS radical scavenging assays revealed silychristin and its analogues to be powerful antioxidants, which were found to be more potent than silybin and 2,3-dehydrosilybin. Compounds 4-6 exhibited inhibition of microsomal lipoperoxidation (IC 4-6 μM). Moreover, compounds 4-8 were found to be almost noncytotoxic for 10 human cell lines of different histogenetic origins. On the basis of these results, compounds 3-6 are likely responsible for most of the antioxidant properties of silymarin attributed traditionally to silybin (silibinin).

BackgroundHierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called ‘cluster heatmap’ is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap.ResultsWe developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust.ConclusionsThe cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-014-0044-4) contains supplementary material, which is available to authorized users.

An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Mor-gan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using K i , K d , IC 50 and EC 50 data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC 50 data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC 50 units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC 50 units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC 50 units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https ://githu b.com/isidr oc/QAFFP _regre ssion .

Experience developing multidisciplinary bachelor’s and master’s curricula involving intertwined chemistry, informatics, and librarianship–editorship skills is described. The bachelor’s curriculum was created in close cooperation of academic staff, library staff, and the publishing house staff (Institute of Chemical Technology Prague: a sole publisher of chemical literature in Czech Republic), with the aim to educate a new generation of information retrieval and e-publishing professionals in the science–technology–medicine (STM) field. This cooperation together with a set of specifically tailored courses gave students the insights into real-world issues of STM publishing. The master’s curriculum, heavily relying on the bachelor’s stage, inclines towards cheminformatics and deepens software engineering skills. Successes, pitfalls, and failures are described, together with proposed changes and future directions.

This study provides a comprehensive and comparative overview of probe sources, structures and targets. The analysis encompasses 4466 chemical probe candidates supported by evidence of specific binding to 796 human proteins.

MicroRNAs (miRNAs) are small RNAs repressing gene expression. They contribute to many physiological processes and pathologies. Consequently, strategies for manipulation of the miRNA pathway are of interest as they could provide tools for experimental or therapeutic interventions. One of such tools could be small chemical compounds identified through high-throughput screening (HTS) with reporter assays. While a number of chemical compounds have been identified in such high-throughput screens, their application potential remains elusive. Here, we report our experience with cell-based HTS of a library of 12,816 chemical compounds to identify miRNA pathway modulators. We used human HeLa and mouse NIH 3T3 cell lines with stably integrated or transiently expressed luciferase reporters repressed by endogenous miR-30 and let-7 miRNAs and identified 163 putative miRNA inhibitors. We report that compounds relieving miRNA-mediated repression via stress induction are infrequent; we have found only two compounds that reproducibly induced stress granules and relieved miRNA-targeted reporter repression. However, we have found that this assay type readily yields non-specific (miRNA-independent) stimulators of luciferase reporter activity. Furthermore, our data provide partial support for previously published miRNA pathway modulators; the most notable intersections were found among anthracyclines, dopamine derivatives, flavones, and stilbenes. Altogether, our results underscore the importance of appropriate negative controls in development of small compound inhibitors of the miRNA pathway. This particularly concerns validation strategies, which would greatly profit from assays that fundamentally differ from the routinely employed miRNA-targeted reporter assays.