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Large-scale ligand-based predictive modelling using support vector machines
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0001-6740-9212
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-6770-0878
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
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2016 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 39Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2016. Vol. 8, article id 39
Keywords [en]
Predictive modelling; Support vector machine; Bioclipse; Molecular signatures; QSAR
National Category
Pharmaceutical Sciences Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-248959DOI: 10.1186/s13321-016-0151-5ISI: 000381186100001PubMedID: 27516811OAI: oai:DiVA.org:uu-248959DiVA, id: diva2:801460
Funder
Swedish National Infrastructure for Computing (SNIC), b2013262 b2015001Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceeSSENCE - An eScience CollaborationAvailable from: 2015-04-09 Created: 2015-04-09 Last updated: 2018-08-28Bibliographically approved
In thesis
1. Ligand-based Methods for Data Management and Modelling
Open this publication in new window or tab >>Ligand-based Methods for Data Management and Modelling
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. p. 73
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 200
Keywords
QSAR, ligand-based drug discovery, bioclipse, information system, cheminformatics, bioinformatics
National Category
Pharmaceutical Sciences Bioinformatics and Systems Biology
Research subject
Pharmaceutical Pharmacology; Bioinformatics
Identifiers
urn:nbn:se:uu:diva-248964 (URN)978-91-554-9237-3 (ISBN)
Public defence
2015-06-05, B22 BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2015-05-12 Created: 2015-04-09 Last updated: 2018-01-11
2. Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web
Open this publication in new window or tab >>Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The pharmaceutical industry is facing a research and development productivity crisis. At the same time we have access to more biological data than ever from recent advancements in high-throughput experimental methods. One suggested explanation for this apparent paradox has been that a crisis in reproducibility has affected also the reliability of datasets providing the basis for drug development. Advanced computing infrastructures can to some extent aid in this situation but also come with their own challenges, including increased technical debt and opaqueness from the many layers of technology required to perform computations and manage data. In this thesis, a number of approaches and methods for dealing with data and computations in early drug discovery in a reproducible way are developed. This has been done while striving for a high level of simplicity in their implementations, to improve understandability of the research done using them. Based on identified problems with existing tools, two workflow tools have been developed with the aim to make writing complex workflows particularly in predictive modelling more agile and flexible. One of the tools is based on the Luigi workflow framework, while the other is written from scratch in the Go language. We have applied these tools on predictive modelling problems in early drug discovery to create reproducible workflows for building predictive models, including for prediction of off-target binding in drug discovery. We have also developed a set of practical tools for working with linked data in a collaborative way, and publishing large-scale datasets in a semantic, machine-readable format on the web. These tools were applied on demonstrator use cases, and used for publishing large-scale chemical data. It is our hope that the developed tools and approaches will contribute towards practical, reproducible and understandable handling of data and computations in early drug discovery.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 68
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 256
Keywords
Reproducibility, Scientific Workflow Management Systems, Workflows, Pipelines, Flow-based programming, Predictive modelling, Semantic Web, Linked Data, Semantic MediaWiki, MediaWiki, RDF, SPARQL, Golang, Reproducerbarhet, Arbetsflödeshanteringssystem, Flödesbaserad programmering, Prediktiv modellering, Semantiska webben, Länkade data, Go
National Category
Pharmacology and Toxicology Bioinformatics (Computational Biology)
Research subject
Bioinformatics; Pharmacology
Identifiers
urn:nbn:se:uu:diva-358353 (URN)978-91-513-0427-4 (ISBN)
Public defence
2018-09-28, Room B22, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 654241Swedish e‐Science Research CentereSSENCE - An eScience Collaboration
Available from: 2018-09-04 Created: 2018-08-28 Last updated: 2018-09-10

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Alvarsson, JonathanLampa, SamuelSchaal, WesleyWikberg, Jarl E. S.Spjuth, Ola

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