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Ligand-Based Target Prediction with Signature Fingerprints
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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8083-2864
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2014 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, no 10, 2647-2653 p.Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2014. Vol. 54, no 10, 2647-2653 p.
National Category
Pharmaceutical Sciences Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-237934DOI: 10.1021/ci500361uISI: 000343849600004PubMedID: 25230336OAI: oai:DiVA.org:uu-237934DiVA: diva2:769682
Funder
Swedish Research Council, VR-2011-6129eSSENCE - An eScience CollaborationScience for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish National Infrastructure for Computing (SNIC)
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2015-05-12Bibliographically 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. 73 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 200
Keyword
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: 2015-07-07

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Alvarsson, JonathanEklund, MartinSpjuth, OlaWikberg, Jarl E. S.

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