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Toward a Benchmarking Data Set Able to Evaluate Ligand- and Structure-based Virtual Screening Using Public HTS Data
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för läkemedelskemi, Avdelningen för organisk farmaceutisk kemi.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för läkemedelskemi, Avdelningen för organisk farmaceutisk kemi.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för läkemedelskemi, Avdelningen för organisk farmaceutisk kemi.ORCID-id: 0000-0001-6770-0878
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för läkemedelskemi, Avdelningen för organisk farmaceutisk kemi.
Vise andre og tillknytning
2015 (engelsk)Inngår i: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 55, nr 2, s. 343-353Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Virtual screening has the potential to accelerate and reduce costs of probe development and drug discovery. To develop and benchmark virtual screening methods, validation data sets are commonly used. Over the years, such data sets have been constructed to overcome the problems of analogue bias and artificial enrichment. With the rapid growth of public domain databases containing high-throughput screening data, such as the PubChem BioAssay database, there is an increased possibility to use such data for validation. In this study, we identify PubChem data sets suitable for validation of both structure- and ligand-based virtual screening methods. To achieve this, high-throughput screening data for which a crystal structure of the bioassay target was available in the PDB were identified. Thereafter, the data sets were inspected to identify structures and data suitable for use in validation studies. In this work, we present seven data sets (MMP13, DUSP3, PTPN22, EPHX2, CTDSP1, MAPK10, and CDK5) compiled using this method. In the seven data sets, the number of active compounds varies between 19 and 369 and the number of inactive compounds between 59 405 and 337 634. This gives a higher ratio of the number of inactive to active compounds than what is found in most benchmark data sets. We have also evaluated the screening performance using docking and 3D shape similarity with default settings. To characterize the data sets, we used physicochemical similarity and 2D fingerprint searches. We envision that these data sets can be a useful complement to current data sets used for method evaluation.

sted, utgiver, år, opplag, sider
American Chemical Society (ACS), 2015. Vol. 55, nr 2, s. 343-353
HSV kategori
Forskningsprogram
Kemi med inriktning mot bioorganisk kemi
Identifikatorer
URN: urn:nbn:se:uu:diva-248018DOI: 10.1021/ci5005465ISI: 000349943100014PubMedID: 25564966OAI: oai:DiVA.org:uu-248018DiVA, id: diva2:798251
Tilgjengelig fra: 2015-03-26 Laget: 2015-03-26 Sist oppdatert: 2018-03-05bibliografisk kontrollert
Inngår i avhandling
1. Computational Methods in Medicinal Chemistry: Mechanistic Investigations and Virtual Screening Development
Åpne denne publikasjonen i ny fane eller vindu >>Computational Methods in Medicinal Chemistry: Mechanistic Investigations and Virtual Screening Development
2015 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2015. s. 65
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 201
Emneord
DFT, IRAP, Virtual Screening, Catalysis, Palladium
HSV kategori
Forskningsprogram
Läkemedelskemi
Identifikatorer
urn:nbn:se:uu:diva-259443 (URN)978-91-554-9293-9 (ISBN)
Disputas
2015-09-25, A1:107a, BMC, Husargatan 3, Uppsala, 09:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2015-09-03 Laget: 2015-08-04 Sist oppdatert: 2018-01-11
2. Computational Modelling in Drug Discovery: Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening
Åpne denne publikasjonen i ny fane eller vindu >>Computational Modelling in Drug Discovery: Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Structure-based drug design and virtual screening are areas of computational medicinal chemistry that use 3D models of target proteins. It is important to develop better methods in this field with the aim of increasing the speed and quality of early stage drug discovery.

The first part of this thesis focuses on the application of structure-based drug design in the search for inhibitors for the protein 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), one of the enzymes in the DOXP/MEP synthetic pathway. This pathway is found in many bacteria (such as Mycobacterium tuberculosis) and in the parasite Plasmodium falciparum.

In order to evaluate and improve current virtual screening methods, a benchmarking data set was constructed using publically available high-throughput screening data. The exercise highlighted a number of problems with current data sets as well as with the use of publically available high-throughput screening data. We hope this work will help guide further development of well designed benchmarking data sets for virtual screening methods.

Conformal prediction is a new method in the computer-aided drug design toolbox that gives the prediction range at a specified level of confidence for each compound. To demonstrate the versatility and applicability of this method we derived models of skin permeability using two different machine learning methods; random forest and support vector machines.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2017. s. 47
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 235
Emneord
drug discovery, docking, virtual screening, tuberculosis, conformal prediction
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-328505 (URN)978-91-513-0049-8 (ISBN)
Disputas
2017-10-13, B/B42, Husargatan 3, Uppsala, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-09-21 Laget: 2017-08-24 Sist oppdatert: 2018-01-13

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