Drug discovery FAQs: workflows for answering multidomain drug discovery questions.

Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.

Scientific competency questions as the basis for semantically enriched open pharmacological space development.

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system.

Open PHACTS: semantic interoperability for drug discovery.

Open PHACTS is a public-private partnership between academia, publishers, small and medium sized enterprises and pharmaceutical companies. The goal of the project is to deliver and sustain an 'open pharmacological space' using and enhancing state-of-the-art semantic web standards and technologies. It is focused on practical and robust applications to solve specific questions in drug discovery research.

Toward interoperable bioscience data.

To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research.

Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine. Recent Semantic Web technologies such as RDF and SPARQL are technical enablers of systems chemical biology, facilitating the deployment of advanced algorithms for searching and mining large integrated datasets. In this paper, we aim to demonstrate how these technologies together can change the way that drug discovery is accomplished.

Visualizing the drug target landscape.

Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets. Assembling a holistic picture of the current landscape of drug discovery activity remains a challenge, however, because of the lack of integration between biological, chemical and clinical resources. Although tools designed to tackle the interpretation of individual data types are abundant, systems that bring together multiple elements to directly enable decision making within drug discovery programmes are rare.

Empowering industrial research with shared biomedical vocabularies.

The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry.

Minimum information about a bioactive entity (MIABE).

Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data - both on licensed and commercially available compounds, and also on those that fail during development - is crucial for understanding how improved molecules could be developed.

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

Background: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.

Results: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records.

Visualizing the drug target landscape.

Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets. Assembling a holistic picture of the current landscape of drug discovery activity remains a challenge, however, because of the lack of integration between biological, chemical and clinical resources. Although tools designed to tackle the interpretation of individual data types are abundant, systems that bring together multiple elements to directly enable decision making within drug discovery programmes are rare.

Drug target central.

Background: One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets.

Objective: Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system.

Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery.

Pharmaceutical research and development is facing substantial challenges that have prompted the industry to shift funding from early- to late-stage projects. Among the effects is a major change in the attitude of many companies to their internal bioinformatics resources: the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain. High-quality, open and accessible data are the foundation of pre-competitive research, and strong public-private partnerships have considerable potential to enhance public data resources, which would benefit everyone engaged in drug discovery.

High-throughput electronic biology: mining information for drug discovery.

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field.

Transgenes encompassing dual-promoter CpG islands from the human TBP and HNRPA2B1 loci are resistant to heterochromatin-mediated silencing.

The genetic elements that are responsible for establishing a transcriptionally competent, open chromatin structure at a region of the genome that consists only of ubiquitously expressed, housekeeping genes are currently unknown. We demonstrate for the first time through functional analysis in stably transfected tissue culture cells that transgenes containing methylation-free CpG islands spanning the dual divergently transcribed promoters from the human TATA binding protein (TBP)-proteasome component-B1 (PSMB1) and heterogeneous nuclear ribonucleoprotein A2/B1 (HNRPA2B1)-heterochromatin protein 1Hs-gamma (chromobox homolog 3, CBX3) gene loci are sufficient to prevent transcriptional silencing and a variegated expression pattern when integrated within centromeric heterochromatin. In addition, only transgene constructs extending over both the HNRPA2B1 and the CBX3 promoters, and not the HNRPA2B1 promoter alone, were able to confer high and stable long-term EGFP reporter gene expression.

Transcriptional regulation of the human TATA binding protein gene.

The human TATA binding protein (TBP) locus consists of a functional domain of three closely linkedhousekeeping genes (TBP, PSMB1 (proteasomal C5 subunit), and PDCD2 (programmed cell death-2)) within a 50-kb interval at chromosome position 6q27. Here we demonstrate that a genomic clone spanning the 20-kb TBP gene, with 12 kb 5' and 3' flanking sequences, was fully functional in stable, transfected L-cells harboring a single copy of this transgene, including after long-term (60 day) culture in the absence of drug selective pressure. Furthermore, we were only able to detect DNaseI hypersensitive sites at the TBP and PSMB1 promoters present within this 44-kb fragment.