Computational models for predicting the activity of small molecules against targets are now routinely developed and used in academia and industry, partially due to public bioactivity databases. While models based on bigger datasets are the trend, recent studies such as chemogenomic active learning have shown that only a fraction of data is needed for effective models in many cases. In this article, the chemogenomic active learning method is discussed and used to newly analyze public databases containing nuclear hormone receptor and cytochrome P450 enzyme family bioactivity. In addition to existing results on kinases and G-protein coupled receptors, results here demonstrate the active learning methodology's effectiveness on extracting informative ligand-target pairs in sparse data scenarios. Experiments to assess the domain of the applicability demonstrate the influence of ligand profiles of similar targets within the family.

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MicroRNAs play a pivotal role in regulating a broad range of biological processes, acting by cleaving mRNAs or by translational repression. A group of plant microRNAs are evolutionarily conserved; however, others are expressed in a species-specific manner. Jute is an agroeconomically important fibre crop; nonetheless, no practical information is available for microRNAs in jute to date. In this study, Illumina sequencing revealed a total of 227 known microRNAs and 17 potential novel microRNA candidates in jute, of which 164 belong to 23 conserved families and the remaining 63 belong to 58 nonconserved families. Among a total of 81 identified microRNA families, 116 potential target genes were predicted for 39 families and 11 targets were predicted for 4 among the 17 identified novel microRNAs. For understanding better the functions of microRNAs, target genes were analyzed by Gene Ontology and their pathways illustrated by KEGG pathway analyses. The presence of microRNAs identified in jute was validated by stem-loop RT-PCR followed by end point PCR and qPCR for randomly selected 20 known and novel microRNAs. This study exhaustively identifies microRNAs and their target genes in jute which will ultimately pave the way for understanding their role in this crop and other crops.

Proteins with Ankyrin repeat motifs (ANK) are found to be associated with diverse biological processes and molecular functions in most of the studied organisms. Several studies have been done on the ANK-motif containing proteins of various model species, but similar studies on their counterparts in brown algae are not available. In this study, we have identified a total of 1,372 ankyrin repeats in 339 proteins of the model brown algae Ectocarpus siliculosus and the consensus sequence of the ANK repeats was determined. The proteins were classified into eight different subfamilies depending on their structural diversity. The data provided in this study may provide useful basis for future reverse genetics analysis of the members of this family.

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The Front Cover shows an emerging method for data mining ligand–target bioactivity matrices known as Chemogenomic Active Learning. As ligand–target pairs labeled as actives and inactives are systematically picked one‐by‐one, a set of decision trees serving as rules to explain active and inactive bioactivity is built. In this article, Rakers et al. show how the method can effectively model nuclear hormone receptor (NHR) and cytochrome P450 (CYP450) family‐wide ligand–target interaction with only a fraction of available data. An actively learned model can be updated after new ligands are synthesized and assayed for activity, converging on hit discovery and optimization more effectively than brute‐force screening. Cover artwork by Christin Rakers and J.B. Brown. More information can be found in the Full Paper by J.B. Brown et al. on page 511 in Issue 6, 2018 (DOI: 10.1002/cmdc.201700677).

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