MOESM2 of Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses

Additional file 2. InChI keys for all compounds in the manually curated set

MOESM1 of Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses

Additional file 1. Manually curated set of 130 compound–target interaction pairs annotated with the earliest patent and publication

MOESM3 of Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses

Additional file 3. Earliest patent in SureChEMBL and earliest publication in ChEMBL for the 130 compounds from the annotated set

Solidarité plus forte : bulletin du Secours populaire français / directeur de publication Marc Domenech

juillet 19911991/07 (N133)

Compression of Molecular Interaction Fields Using Wavelet Thumbnails: Application to Molecular Alignment

Molecular interaction fields provide a useful description of ligand binding propensity and have found widespread use in computer-aided drug design, for example, to characterize protein binding sites and in small molecular applications, such as three-dimensional quantitative structure–activity relationships, physicochemical property prediction, and virtual screening. However, the grids on which the field data are stored are typically very large, consisting of thousands of data points, which make them cumbersome to store and manipulate. The wavelet transform is a commonly used data compression technique, for example, in signal processing and image compression. Here we use the wavelet transform to encode molecular interaction fields as wavelet thumbnails, which represent the original grid data in significantly reduced volumes. We describe a method for aligning wavelet thumbnails based on extracting extrema from the thumbnails and subsequently use them for virtual screening. We demonstrate that wavelet thumbnails provide an effective method of capturing the three-dimensional information encoded in a molecular interaction field

The Application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to Support Drug Discovery Research

<div><p>Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.</p></div

Benefits of using the Open PHACTS Discovery Platform for drug discovery research.

<p>Benefits of using the Open PHACTS Discovery Platform for drug discovery research.</p

Regulators of Vitamin D signaling obtained from Workflow 3.

<p>Terms in bold are discussed in the text.</p><p>Regulators of Vitamin D signaling obtained from Workflow 3.</p

Use case A workflow.

<p>Schematic representation of the workflow for use case A. Starting with a free text search for the desired target(s), Uniprot AC identifiers, protein sequences and gene symbols are obtained using ‘Free Text to Concept’ and ‘Target Information’ API calls. A gene symbol list is obtained for targets from the same family (based on GO) using a ‘Target Classification’ API call. Alternatively, UniProt ACs obtained for related protein sequences via a BLAST search are used to get corresponding gene symbols using the ‘Target Information’ API call. Using this gene list, corresponding pharmacology records in the public domain are obtained via the ‘Pharmacology by Target’ API. In parallel, the gene symbol list is used to retrieve target pharmacology information in Thomson Reuters Integrity, World Drug Index, PharmaProjects, GVKBio GOSTAR, and Janssen pharmacology proprietary databases. Public pharmacology records (additional targets) for the retrieved compounds are then obtained using the ‘Pharmacology by compound’ API call with equivalent searches in Janssen pharmacology proprietary databases. If required, a structure similarity search is performed with the retrieved compounds to identify additional compounds, followed by another round of searches in Open PHACTS and proprietary databases as before. A Pipeline Pilot script was developed to run the above steps and produce an integrated list of compounds, activity data and target information from all databases. Proprietary components developed at Janssen were used to parse Janssen pharmacology data. All data processing was performed within the Pipeline Pilot framework.</p

Use case C workflows 3 and 4.

<p>Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other operations are shown in yellow boxes. Results obtained after these operations are shown in light grey boxes. Blue colored boxes show results included in the manuscript. Sample input URLs are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115460#pone.0115460.s005" target="_blank">S2 Table</a>. For workflow 3, Urls for all species orthologues of a given target were obtained using ‘Free Text to Concept for Semantic Tag’ API. Pharmacology data for these orthologues was obtained using ‘Target Pharmacology: List’ API. Data was limited to compounds tested in binding affinity assays from bovine, porcine and human in both VDR and DBP by applying appropriate filters in KNIME. For workflow 4, GO terms related to ‘Regulation of Vitamin D’ were obtained using the ‘Free Text to Concept’ API. Children of these GO terms were obtained using ‘Hierarchies: Child Nodes’ API. The data were sorted by positive/negative regulation. Gene products associated with these GO terms were obtained using ‘Target Class Member: List’ API.</p