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Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
2012 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 52, no 1, 225-232 p.Article in journal (Refereed) Published
Abstract [en]

Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.

Place, publisher, year, edition, pages
2012. Vol. 52, no 1, 225-232 p.
National Category
Medicinal Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-169381DOI: 10.1021/ci2004835ISI: 000299351600021OAI: oai:DiVA.org:uu-169381DiVA: diva2:506254
Available from: 2012-02-28 Created: 2012-02-28 Last updated: 2015-10-01Bibliographically approved
In thesis
1. Computational Methods in Medicinal Chemistry: Mechanistic Investigations and Virtual Screening Development
Open this publication in new window or tab >>Computational Methods in Medicinal Chemistry: Mechanistic Investigations and Virtual Screening Development
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Computational methods have become an integral part of drug development and can help bring new and better drugs to the market faster. The process of predicting the biological activity of large compound collections is known as virtual screening, and has been instrumental in the development of several drugs today in the market. Computational methods can also be used to elucidate the energies associated with chemical reactivity and predict how to improve a synthetic protocol. These two applications of computational medicinal chemistry is the focus of this thesis.

In the first part of this work, quantum mechanics has been used to probe the energy surface of palladium(II)-catalyzed decarboxylative reactions in order to gain a better understating of these systems (paper I-III). These studies have mapped the reaction pathways and been able to make accurate predictions that were verified experimentally.

The other focus of this work has been to develop virtual screening methodology. Our first study in the area (paper IV) investigated if the results from several virtual screening methods could be combined using data fusion techniques in order to get a more consistent result and better performance. The study showed that the results obtained from data fusion were more consistent than the results from any single method. The data fusion methods also for several target had a better performance than any of the included single methods.

Next, we developed a dataset suitable for evaluating the performance of virtual screening methods when applied to large compound collection as a replacement or complement for high throughput screening (paper V). This is the first benchmark dataset of its kind.

Finally, a method for using computationally derived reaction coordinates as basis for virtual screening was developed. The aim was to find inhibitors that resemble key steps in the mechanism (paper VI). This initial proof of concept study managed to locate several known and one previously not reported reaction mimetics against insulin regulated amino peptidase.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 65 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 201
Keyword
DFT, IRAP, Virtual Screening, Catalysis, Palladium
National Category
Medicinal Chemistry Organic Chemistry
Research subject
Medicinal Chemistry
Identifiers
urn:nbn:se:uu:diva-259443 (URN)978-91-554-9293-9 (ISBN)
Public defence
2015-09-25, A1:107a, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2015-09-03 Created: 2015-08-04 Last updated: 2015-10-01

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Svensson, FredrikKarlén, AndersSköld, Christian

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