WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

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The solute carrier (SLC) superfamily represents the biggest family of transporters with important roles in health and disease. Despite being attractive and druggable targets, the majority of SLCs remains understudied. One major hurdle in research on SLCs is the lack of tools, such as cell-based assays to investigate their biological role and for drug discovery. Another challenge is the disperse and anecdotal information on assay strategies that are suitable for SLCs. This review provides a comprehensive overview of state-of-the-art cellular assay technologies for SLC research and discusses relevant SLC characteristics enabling the choice of an optimal assay technology. The Innovative Medicines Initiative consortium RESOLUTE intends to accelerate research on SLCs by providing the scientific community with high-quality reagents, assay technologies and data sets, and to ultimately unlock SLCs for drug discovery.

BackgroundThe human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) and Multidrug Resistance Protein 1 (P-gp) are co-expressed in many tissues and barriers, especially at the blood–brain barrier and at the hepatocyte canalicular membrane. Understanding their interplay in affecting the pharmacokinetics of drugs is of prime interest. In silico tools to predict inhibition and substrate profiles towards BCRP and P-gp might serve as early filters in the drug discovery and development process. However, to build such models, pharmacological data must be collected for both targets, which is a tedious task, often involving manual and poorly reproducible steps.ResultsCompounds with inhibitory activity measured against BCRP and/or P-gp were retrieved by combining Open Data and manually curated data from literature using a KNIME workflow. After determination of compound overlap, machine learning approaches were used to establish multi-label classification models for BCRP/P-gp. Different ways of addressing multi-label problems are explored and compared: label-powerset, binary relevance and classifiers chain. Label-powerset revealed important molecular features for selective or polyspecific inhibitory activity. In our dataset, only two descriptors (the numbers of hydrophobic and aromatic atoms) were sufficient to separate selective BCRP inhibitors from selective P-gp inhibitors. Also, dual inhibitors share properties with both groups of selective inhibitors. Binary relevance and classifiers chain allow improving the predictivity of the models.ConclusionsThe KNIME workflow proved a useful tool to merge data from diverse sources. It could be used for building multi-label datasets of any set of pharmacological targets for which there is data available either in the open domain or in-house. By applying various multi-label learning algorithms, important molecular features driving transporter selectivity could be retrieved. Finally, using the dataset with missing annotations, predictive models can be derived in cases where no accurate dense dataset is available (not enough data overlap or no well balanced class distribution).Graphical abstract.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0121-y) contains supplementary material, which is available to authorized users.