| 1. |
- O'Boyle, Noel, et al.
(författare)
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Open Data, Open Source and Open Standards in chemistry : The Blue Obelisk five years on
- 2011
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Ingår i: Journal of Cheminformatics. - : BioMed Central. - 1758-2946. ; 3, s. 37-
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Tidskriftsartikel (refereegranskat)abstract
- Background: The Blue Obelisk movement was established in 2005 as a response to the lack of Open Data,Open Standards and Open Source (ODOSOS) in chemistry. It aims to make it easier to carry out chemistryresearch by promoting interoperability between chemistry software, encouraging cooperation between OpenSource developers, and developing community resources and Open Standards. Results: This contribution looks back on the work carried out by the Blue Obelisk in the past 5 years and surveysprogress and remaining challenges in the areas of Open Data, Open Standards, and Open Source in chemistry. Conclusions: We show that the Blue Obelisk has been very successful in bringing together researchers anddevelopers with common interests in ODOSOS, leading to development of many useful resources freely availableto the chemistry community
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| 2. |
- Samwald, Matthias, et al.
(författare)
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Linked open drug data for pharmaceutical research and development
- 2011
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 3, s. 19-
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Tidskriftsartikel (refereegranskat)abstract
- There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.
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| 3. |
- Spjuth, Ola, 1977-, et al.
(författare)
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Applications of the InChI in cheminformatics with the CDK and Bioclipse
- 2013
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 5:14
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundThe InChI algorithms are written in C++ and not available as Java library. Integration into softwarewritten in Java therefore requires a bridge between C and Java libraries, provided by the Java NativeInterface (JNI) technology.ResultsWe here describe how the InChI library is used in the Bioclipse workbench and the Chemistry Development Kit (CDK) cheminformatics library. To make this possible, a JNI bridge to the InChIlibrary was developed, JNI-InChI, allowing Java software to access the InChI algorithms. By usingthis bridge, the CDK project packages the InChI binaries in a module and offers easy access fromJava using the CDK API. The Bioclipse project packages and offers InChI as a dynamic OSGi bundlethat can easily be used by any OSGi-compliant software, in addition to the regular Java Archive andMaven bundles. Bioclipse itself uses the InChI as a key component and calculates it on the fly whenvisualizing and editing chemical structures. We demonstrate the utility of InChI with various applications in CDK and Bioclipse, such as decision support for chemical liability assessment, tautomergeneration, and for knowledge aggregation using a linked data approach.ConclusionsThese results show that the InChI library can be used in a variety of Java library dependency solutions, making the functionality easily accessible by Java software, such as in the CDK. The applications show various ways the InChI has been used in Bioclipse, to enrich its functionality.
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| 4. |
- Spjuth, Ola, 1977-, et al.
(författare)
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Towards interoperable and reproducible QSAR analyses : Exchange of data sets
- 2010
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Ingår i: Journal of Cheminformatics. - : BioMed Central. - 1758-2946. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: QSAR/QSPR is a widely used method to relate chemical structures and responses based on ex- perimental observations. In QSAR, chemical structures are expressed as descriptors, which are mathematical representations like calculated properties or enumerated fragments. Many existing QSAR data sets are based on a combination of different software tools mixed with in-house developed solutions, with datasets manually assembled in spreadsheets. Currently there exists no agreed-upon definition of descriptors and no standard for exchanging data sets in QSAR, which together with numerous different descriptor implementations makes it a virtually impossible task to reproduce and validate analyses, and significantly hinders collaborations and re-use of data.RESULTS: We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR/QSPR data sets, comprising an open XML format (QSAR-ML) and an open extensible descriptor ontology (Blue Obelisk Descriptor Ontology). The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a data set described by QSAR-ML makes its setup completely reproducible. We also provide an implementation as a set of plugins for Bioclipse that simplifies QSAR data set formation, and allows for exporting in QSAR-ML as well as traditional CSV formats. The implementation facilitates addition of new descriptor implementations, from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services.CONCLUSIONS: Standardized QSAR data sets opens up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible dataset formation, solving the problems of defining which software components were used, their versions, and the case of multiple names for the same descriptor. This makes is easy to join, extend, combine data sets and also to work collectively. The presented Bioclipse plugins equip scientists with intuitive tools that make QSAR-ML widely available for the community.
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| 5. |
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| 6. |
- Willighagen, Egon L, et al.
(författare)
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The ChEMBL database as linked open data
- 2013
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 5:1, s. 23-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Making data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes thedata machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis. Results: This paper describes recent developments in an ongoing project converting data from the ChEMBL database into RDF triples. Relative to earlier versions, this updated version of ChEMBL-RDF uses recently introduced ontologies, including CHEMINF and CiTO; exposes more information from the database; and is now available as dereferencable,linked data. To demonstrate these new features, we present novel use cases showing further integration with other web resources, including Bio2RDF, Chem2Bio2RDF, and ChemSpider, and showing the use of standard ontologies for querying. Conclusions: We have illustrated the advantages of using open standards and ontologies to link the ChEMBL database to other databases. Using those links and the knowledge encoded in standards and ontologies, theChEMBL-RDF resource creates a foundation for integrated semantic web cheminformatics applications, such as the presented decision support.
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| 7. |
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| 8. |
- Öberg, Tomas, et al.
(författare)
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Updating existing QSAR models: selection and weighting of new data
- 2010
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Ingår i: Journal of Cheminformatics. - 1758-2946. ; 2:Suppl 1, s. P19-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Computational chemistry and quantitative structure-activity relationships (QSAR) are foreseen to be extensively used in the implementation of the new REACH regulation for chemicals in Europe. However, for some compound groups the data are too few in number to permit both calibration and testing of a new model. Usage and previously developed or updated models are then viable alternatives.Perfluorocarboxylic acids (PFCAs) and fluoroteleomer alcohols (FTOHs) are two groups of environmentally relevant compounds, with unique physical and chemical properties. The subcooled liquid vapour pressure (pL) is one such property, where experimental determinations are limited and far from consistent [1]. Updating is, however, challenging when the new compounds are far outside of the original calibration domain space. But by carefully selecting and weighting only three new compounds, we have been able to update a previously developed general QSAR model [2], to cover the new domain while maintaining predictive performance for the earlier calibration and test data. The optimal weighting scheme was determined from the sample leverages and residuals in the calibration phase [3].The performance of this re-calibrated model greatly surpassed previous modelling attempts [4], when applied to an external test set of two PFCAs and four FTOHs with pL in the range 0.2-200 Pa; with Q2Ext = 0.994 and RMSEP = 0.190 units of log Pa. The domain coverage also increased from 1% to 51%, for 426 perfluoroalkylated compounds selected from the REACH registration list, the PhysProp database, and the OECD 2006 survey [5]. Selection and weighting of new calibration data can thus facilitate the extension and use of existing QSAR models. This investigation was supported by the EU FP7 project CADASTER (grant agreement no. 212668).
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| 9. |
- Napolitano, F, et al.
(författare)
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Drug repositioning: a machine-learning approach through data integration
- 2013
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Ingår i: Journal of cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 5:1, s. 30-
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Tidskriftsartikel (refereegranskat)abstract
- Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.
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| 10. |
- Aghdam, Rosa, et al.
(författare)
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Using informative features in machine learning based method for COVID-19 drug repurposing
- 2021
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Ingår i: Journal of Cheminformatics. - : Springer Nature. - 1758-2946. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.
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