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  • 1.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ligand-based Methods for Data Management and Modelling2015Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface. 

    The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed.

    An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.

  • 2.
    Alvarsson, Jonathan
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Andersson, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Larsson, Rolf
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk farmakologi.
    Wikberg, Jarl
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Brunn: an open source laboratory information system for microplates with a graphical plate layout design process2011Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 12, nr 1, artikkel-id 179Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background:

    Compound profiling and drug screening generates large amounts of data and is generally based on microplate assays. Current information systems used for handling this are mainly commercial, closed source, expensive, and heavyweight and there is a need for a flexible lightweight open system for handling plate design, and validation and preparation of data.

    Results:

    A Bioclipse plugin consisting of a client part and a relational database was constructed. A multiple-step plate layout point-and-click interface was implemented inside Bioclipse. The system contains a data validation step, where outliers can be removed, and finally a plate report with all relevant calculated data, including dose-response curves.

    Conclusions:

    Brunn is capable of handling the data from microplate assays. It can create dose-response curves and calculate IC50 values. Using a system of this sort facilitates work in the laboratory. Being able to reuse already constructed plates and plate layouts by starting out from an earlier step in the plate layout design process saves time and cuts down on error sources.

  • 3.
    Alvarsson, Jonathan
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Andersson, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Cancerfarmakologi och beräkningsmedicin.
    Carlsson, Lars
    AstraZeneca R&D.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wikberg, Jarl E. S.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines2014Inngår i: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, nr 11, s. 3211-3217Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, ?, and signature height. C is a penalty parameter that limits overfitting, ? controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and gamma in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.

  • 4.
    Alvarsson, Jonathan
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Engkvist, Ola
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Carlsson, Lars
    Wikberg, Jarl E. S.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Noeske, Tobias
    Ligand-Based Target Prediction with Signature Fingerprints2014Inngår i: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, nr 10, s. 2647-2653Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.

  • 5.
    Alvarsson, Jonathan
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Lampa, Samuel
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Schaal, Wesley
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Andersson, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Cancerfarmakologi och beräkningsmedicin.
    Wikberg, Jarl E. S.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Large-scale ligand-based predictive modelling using support vector machines2016Inngår i: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, artikkel-id 39Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

  • 6.
    Blom, Kristin
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Biokemisk struktur och funktion.
    Nygren, Peter
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Cancerfarmakologi och beräkningsmedicin. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Experimentell och klinisk onkologi.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Larsson, Rolf
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Cancerfarmakologi och beräkningsmedicin.
    Andersson, Claes R.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Cancerfarmakologi och beräkningsmedicin.
    Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy2016Inngår i: JALA, ISSN 2211-0682, Vol. 21, nr 1, s. 178-187Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Although medical cancer treatment has improved during the past decades, it is difficult to choose between several first-line treatments supposed to be equally active in the diagnostic group. It is even more difficult to select a treatment after the standard protocols have failed. Any guidance for selection of the most effective treatment is valuable at these critical stages. We describe the principles and procedures for ex vivo assessment of drug activity in tumor cells from patients as a basis for tailored cancer treatment. Patient tumor cells are assayed for cytotoxicity with a panel of drugs. Acoustic drug dispensing provides great flexibility in the selection of drugs for testing; currently, up to 80 compounds and/or combinations thereof may be tested for each patient. Drug response predictions are obtained by classification using an empirical model based on historical responses for the diagnosis. The laboratory workflow is supported by an integrated system that enables rapid analysis and automatic generation of the clinical referral response.

  • 7.
    Kensert, Alexander
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Norinder, Ulf
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Evaluating parameters for ligand-based modeling with random forest on sparse data sets2018Inngår i: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, artikkel-id 49Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints (p <= 0.05), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint's radius.

  • 8.
    Lampa, Samuel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Arvidsson Mc Shane, Staffan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Berg, Arvid
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ahlberg, Ernst
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Predicting off-target binding profiles with confidence using Conformal Prediction2018Inngår i: Frontiers in Pharmacology, ISSN 1663-9812, E-ISSN 1663-9812, Vol. 9, artikkel-id 1256Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only providing point predictions. We here describe the use of conformal prediction for predicting off-target interactions with models trained on data from 31 targets in the ExCAPE dataset, selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence p-values for each class. The full pre-processing and model training process is openly available as scientific workflows on GitHub, rendering it fully reproducible. We illustrate the usefulness of the methodology on a set of compounds extracted from DrugBank. The resulting models are published online and are available via a graphical web interface and an OpenAPI interface for programmatic access.

  • 9.
    Lampa, Samuel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles2016Inngår i: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, artikkel-id 67Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.

  • 10.
    Lampa, Samuel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Dahlö, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    SciPipe - Turning Scientific Workflows into Computer Programs2019Inngår i: Computing in science & engineering (Print), ISSN 1521-9615, E-ISSN 1558-366X, Vol. 21, nr 3, s. 109-113Artikkel i tidsskrift (Fagfellevurdert)
  • 11.
    Lampa, Samuel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab. Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
    Dahlö, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines2019Inngår i: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, nr 5, artikkel-id giz044Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. Findings: SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. Conclusions: SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.

  • 12.
    Lapins, Maris
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Arvidsson, Staffan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Lampa, Samuel
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Berg, Arvid
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Schaal, Wesley
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    A confidence predictor for logD using conformal regression and a support-vector machine2018Inngår i: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, nr 1, artikkel-id 17Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

  • 13.
    O'Boyle, Noel
    et al.
    University College Cork.
    Guha, Rajarshi
    NIH Center for Translational Therapeutic.
    Willighagen, Egon
    Karolinska Institutet.
    Adams, Samuel
    University of Cambridge.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Bradley, Jean-Claude
    Drexel University.
    Filippov, Igor
    NCI-Frederick.
    Hansson, Robert
    St. Olaf College.
    Hanwell, Marcus
    Kitware, Inc.
    Hutchison, Geoffrey
    University of Pittsburg.
    James, Craig
    eMolecules Inc.
    Jeliazkova, Nina
    Ideaconsult Ltd.
    Lang, Andrew
    Oral Roberts University.
    Langner, Karol
    Leiden University.
    Lonie, David
    State University of New York at Buffalo.
    Lowe, Daniel
    University of Cambridge.
    Pansanel, Jerome
    Université de Strasbourg.
    Pavlov, Dmitry
    GGA Software Service.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Steinbeck, Christoph
    European Bioinformatics Institute.
    Tenderholt, Adam
    University of Washington.
    Thiesen, Kevin
    Chemlabs.
    Murray-Rust, Peter
    University of Cambridge.
    Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on2011Inngår i: Journal of Cheminformatics, ISSN 1758-2946, Vol. 3, s. 37-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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

  • 14.
    Spjuth, Ola
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Bioclipse 2: Towards integrated biocheminformatics2009Inngår i: EMBnet.news, ISSN 1023-4144, Vol. 15, nr 3, s. 25-27Artikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
  • 15.
    Spjuth, Ola
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Carlsson, Lars
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Georgiev, Valentin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Willighagen, Egon
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Open source drug discovery with Bioclipse2012Inngår i: Current Topics in Medicinal Chemistry, ISSN 1568-0266, E-ISSN 1873-4294, Vol. 12, nr 18, s. 1980-1986Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    We present the open source components for drug discovery that has been developed and integrated into the graphical workbench Bioclipse. Building on a solid open source cheminformatics core, Bioclipse has advanced functionality for managing and visualizing chemical structures and related information. The features presented here include QSAR/QSPR modeling, various predictive solutions such as decision support for chemical liability assessment, site-of-metabolism prediction, virtual screening, and knowledge discovery and integration. We demonstrate the utility of the described tools with examples from computational pharmacology, toxicology, and ADME. Bioclipse is used in both academia and industry, and is a good example of open source leading to new solutions for drug discovery.

  • 16.
    Spjuth, Ola
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Georgiev, Valentin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Carlsson, Lars
    Global Safety Assesment, AstraZeneca R&D.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Berg, Arvid
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Willighagen, Egon
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Wikberg, Jarl E S
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Bioclipse-R: Integrating management and visualization of life science data with statistical analysis2013Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 29, nr 2, s. 286-289Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Bioclipse, a graphical workbench for the life sciences, provides functionality for managing and visualizing life science data. We introduce Bioclipse-R, which integrates Bioclipse and the statistical programming language R. The synergy between Bioclipse and R is demonstrated by the construction of a decision support system for anticancer drug screening and mutagenicity prediction, which shows how Bioclipse-R can be used to perform complex tasks from within a single software system.

  • 17.
    Torabi Moghadam, Behrooz
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Holm, Marcus
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Tillämpad beräkningsvetenskap.
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Carlsson, Lars
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Scaling predictive modeling in drug development with cloud computing2015Inngår i: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 55, s. 19-25Artikkel i tidsskrift (Fagfellevurdert)
  • 18.
    Wikberg, Jarl
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Willighagen, Egon
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Lapins, Maris
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Engkvist, Ola
    AstraZeneca R&D, Sweden.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Introduction to Pharmaceutical Bioinformatics2010 (oppl. 2)Bok (Annet vitenskapelig)
  • 19.
    Willighagen, Egon
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Andersson, Annsofie
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Cancerfarmakologi och beräkningsmedicin.
    Eklund, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Lampa, Samuel
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Lapins, Maris
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Wikberg, Jarl
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Linking the Resource Description Framework to cheminformatics and proteochemometrics2011Inngår i: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 2, nr Suppl 1, s. 6-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND :

    Semantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but have not found widespread use yet. The semantic web technology Resource Description Framework (RDF) and related methods show to be sufficiently versatile to change that situation.

    RESULTS :

    The work presented here focuses on linking RDF approaches to existing molecular chemometrics fields, including cheminformatics, QSAR modeling and proteochemometrics. Applications are presented that link RDF technologies to methods from statistics and cheminformatics, including data aggregation, visualization, chemical identification, and property prediction. They demonstrate how this can be done using various existing RDF standards and cheminformatics libraries. For example, we show how IC50 and Ki values are modeled for a number of biological targets using data from the ChEMBL database.

    CONCLUSIONS :

    We have shown that existing RDF standards can suitably be integrated into existing molecular chemometrics methods. Platforms that unite these technologies, like Bioclipse, makes this even simpler and more transparent. Being able to create and share workflows that integrate data aggregation and analysis (visual and statistical) is beneficial to interoperability and reproducibility. The current work shows that RDF approaches are sufficiently powerful to support molecular chemometrics workflows.

  • 20.
    Willighagen, Egon L.
    et al.
    Maastricht Univ, Dept Bioinformat, NUTRIM, BiGCaT, NL-6200 MD Maastricht, Netherlands..
    Mayfield, John W.
    NextMove Software Ltd, Cambridge CB4 0EY, England..
    Alvarsson, Jonathan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Berg, Arvid
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Carlsson, Lars
    AstraZeneca, Innovat Med & Early Dev, Quantitat Biol, Molndal, Sweden..
    Jeliazkova, Nina
    Ideaconsult Ltd, A Kanchev 4, Sofia 1000, Bulgaria..
    Kuhn, Stefan
    Univ Leicester, Dept Informat, Leicester, Leics, England..
    Pluskal, Tomas
    Whitehead Inst Biomed Res, 455 Main St, Cambridge, MA 02142 USA..
    Rojas-Cherto, Miquel
    Quim Clin Aplicada, Amposta 43870, Spain..
    Spjuth, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. 4 Hanway Pl, London W1T 1HD, England..
    Torrance, Gilleain
    4 Hanway Pl, London W1T 1HD, England..
    Evelo, Chris T.
    Maastricht Univ, Dept Bioinformat, NUTRIM, BiGCaT, NL-6200 MD Maastricht, Netherlands..
    Guha, Rajarshi
    Natl Ctr Adv Translat Sci, 9800 Med Ctr Dr, Rockville, MD 20850 USA..
    Steinbeck, Christoph
    Friedrich Schiller Univ, Inst Inorgan & Analyt Chem, Lessingstr 8, D-07743 Jena, Germany..
    The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching2017Inngår i: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 9, artikkel-id 33Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms.

    Results: We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism.

    Conclusions: This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.

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