Abstract: : Working with small-molecule datasets is a routine task for cheminformaticians and chemists. The analysis and comparison of vendor catalogues and the compilation of promising candidates as starting points for screening campaigns are but a few very common applications. The workflows applied for this purpose usually consist of multiple basic cheminformatics tasks such as checking for duplicates or filtering by physico-chemical properties. Pipelining tools allow to create and change such workflows without much effort, but usually do not support interventions once the pipeline has been started. In many contexts, however, the best suited workflow is not known in advance, thus making it necessary to take the results of the previous steps into consideration before proceeding.To support intuition-driven processing of compound collections, we developed MONA, an interactive tool that has been designed to prepare and visualize large small-molecule datasets. Using an SQL database common cheminformatics tasks such as analysis and filtering can be performed interactively with various methods for visual support. Great care was taken in creating a simple, intuitive user interface which can be instantly used without any setup steps. MONA combines the interactivity of molecule database systems with the simplicity of pipelining tools, thus enabling the case-to-case application of chemistry expert knowledge. The current version is available free of charge for academic use and can be downloaded at http://www.zbh.uni-hamburg.de/mona.

Abstract: MOTIVATION: The interaction between drugs and their targets, often proteins, and between antibodies and their targets, is important for planning and analyzing investigational and therapeutic interventions in many biological systems. Although drug-target and antibody-target datasets are available in separate databases, they are not publicly available in an integrated bioinformatics resource. As medical therapeutics, especially in cancer, increasingly uses targeted drugs and measures their effects on biomolecular profiles, there is an unmet need for a user-friendly toolset that allows researchers to comprehensively and conveniently access and query information about drugs, antibodies and their targets. SUMMARY: The PiHelper framework integrates human drug-target and antibody-target associations from publicly available resources to help meet the needs of researchers in systems pharmacology, perturbation biology and proteomics. PiHelper has utilities to (i) import drug- and antibody-target information; (ii) search the associations either programmatically or through a web user interface (UI); (iii) visualize the data interactively in a network; and (iv) export relationships for use in publications or other analysis tools. AVAILABILITY: PiHelper is a free software under the GNU Lesser General Public License (LGPL) v3.0. Source code and documentation are at http://bit.ly/pihelper. We plan to coordinate contributions from the community by managing future releases.

Abstract: MOTIVATION: Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than approximately 100 nodes and approximately 200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a node's brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses. AVAILABILITY: N2C is freely available at http://www.maayanlab.net/N2C and is open source. CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: OBJECTIVE: To identify and highlight the feasibility, challenges, and advantages of providing a cross-domain pipeline that can link relevant biodiversity information for phyto-therapeutic assessment. MATERIALS AND METHODS: A public repository of clinical trials information (ClinicalTrials.gov) was explored to determine the state of plant-based interventions under investigation. RESULTS: The results showed that approximately 15% of drug interventions in ClinicalTrials.gov were potentially plant related, with about 60% of them clustered within 10 taxonomic families. Further analysis of these plant-based interventions identified approximately 3.7% of associated plant species as endangered as determined from the International Union for the Conservation of Nature Red List. DISCUSSION: The diversity of the plant kingdom has provided human civilization with life-sustaining food and medicine for centuries. There has been renewed interest in the investigation of botanicals as sources of new drugs, building on traditional knowledge about plant-based medicines. However, data about the plant-based biodiversity potential for therapeutics (eg, based on genetic or chemical information) are generally scattered across a range of sources and isolated from contemporary pharmacological resources. This study explored the potential to bridge biodiversity and biomedical knowledge sources. CONCLUSIONS: The findings from this feasibility study suggest that there is an opportunity for developing plant-based drugs and further highlight taxonomic relationships between plants that may be rich sources for bioprospecting.

Abstract: With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly important role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate the convenient network analysis of diseases, therapies, genes and drugs. With improved understanding of the mechanisms of complex diseases and drug actions through network analysis, novel drug methods (e.g., drug repositioning, multi-target drug and combination therapy) can be designed. More specifically, the new update includes (i) integrated search and navigation of disease and drug hierarchies; (ii) integrated disease-gene, therapy-drug and drug-target association to aid the network construction and filtering; (iii) annotation of genes/drugs using disease/therapy information; (iv) prediction of associated diseases/therapies for a given set of genes/drugs using enrichment analysis; (v) network transformation to support construction of versatile network of drugs, genes, diseases and therapies; (vi) enhanced user interface using docking windows to allow easy customization of node and edge properties with build-in legend node to distinguish different node type. VisANT is freely available at: http://visant.bu.edu.

Abstract: Compound promiscuity refers to the ability of small molecules to specifically interact with multiple targets, which represents the origin of polypharmacology. Promiscuity is thought to be a widespread characteristic of pharmaceutically relevant compounds. Yet, the degree of promiscuity among active compounds from different sources remains uncertain. Here, we report a thorough analysis of compound promiscuity on the basis of more than 1,000 PubChem confirmatory bioassays, which yields an upper-limit assessment of promiscuity among active compounds. Because most PubChem compounds have been tested in large numbers of assays, data sparseness has not been a limiting factor for the current analysis. We have determined that there is an overall likelihood of approximately 50% of an active PubChem compound to interact with two or more targets. The probability to interact with more than five targets is reduced to 7.6%. On average, an active PubChem compound was found to interact with approximately 2.5 targets. Moreover, if only activities consistently detected in all assays available for a given target were considered, this ratio was further reduced to approximately 2.3 targets per compound. For comparison, we have also analyzed high-confidence activity data from ChEMBL, the major public repository of compounds from medicinal chemistry, and determined that an active ChEMBL compound interacted on average with only approximately 1.5 targets. Taken together, our results indicate that the degree of compound promiscuity is lower than often assumed.

Abstract: Biological events such as gene expression, regulation, phosphorylation, localization and protein catabolism play important roles in the development of diseases. Understanding the association between diseases and genes can be enhanced with the identification of involved biological events in this association. Although biological knowledge has been accumulated in several databases and can be accessed through the Web, there is no specialized Web tool yet allowing for a query into the relationship among diseases, genes and biological events. For this task, we developed DigSee to search MEDLINE abstracts for evidence sentences describing that 'genes' are involved in the development of 'cancer' through 'biological events'. DigSee is available through http://gcancer.org/digsee.

Abstract: BACKGROUND: The Gene Ontology (GO) facilitates the description of the action of gene products in a biological context. Many GO terms refer to chemical entities that participate in biological processes. To facilitate accurate and consistent systems-wide biological representation, it is necessary to integrate the chemical view of these entities with the biological view of GO functions and processes. We describe a collaborative effort between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure that the representation of chemicals in the GO is both internally consistent and in alignment with the chemical expertise captured in ChEBI. RESULTS: We have examined and integrated the ChEBI structural hierarchy into the GO resource through computationally-assisted manual curation of both GO and ChEBI. Our work has resulted in the creation of computable definitions of GO terms that contain fully defined semantic relationships to corresponding chemical terms in ChEBI. CONCLUSIONS: The set of logical definitions using both the GO and ChEBI has already been used to automate aspects of GO development and has the potential to allow the integration of data across the domains of biology and chemistry. These logical definitions are available as an extended version of the ontology from http://purl.obolibrary.org/obo/go/extensions/go-plus.owl.

Abstract: SUMMARY: Systematic studies of drug repositioning require the integration of multi-level drug data, including basic chemical information (such as SMILES), drug targets, target-related signaling pathways, clinical trial information and Food and Drug Administration (FDA)-approval information, to predict new potential indications of existing drugs. Currently available databases, however, lack query support for multi-level drug information and thus are not designed to support drug repositioning studies. DrugMap Central (DMC), an online tool, is developed to help fill the gap. DMC enables the users to integrate, query, visualize, interrogate, and download multi-level data of known drugs or compounds quickly for drug repositioning studies all within one system. AVAILABILITY: DMC is accessible at http://r2d2drug.org/DMC.aspx. CONTACT: STWong@tmhs.org.

Abstract: MOTIVATION: In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. RESULTS: We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. AVAILABILITY: Software and datasets are available on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.