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Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.ORCID iD: 0000-0002-4420-772X
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
Karolinska Inst, Unit Toxicol Sci, Swetox, Forskargatan 20, SE-15136 Sodertalje, Sweden.;Stockholm Univ, Dept Comp & Syst Sci, Forum 100, SE-16440 Kista, Sweden..
2017 (English)In: Molecular Pharmaceutics, ISSN 1543-8384, E-ISSN 1543-8392, Vol. 14, no 5, 1571-1576 p.Article in journal (Refereed) Published
Abstract [en]

Skin serves as a drug administration route, and skin permeability of chemicals is of significant interest in the pharmaceutical and cosmetic industries. An aggregated conformal prediction (ACP) framework was used to build models, for predicting the permeation rate (log K-p) of chemical compounds through human skin. The conformal prediction method gives as an output the prediction range at a given level of confidence for each compound, which enables the user to make a more informed decision when, for example, suggesting the next compound to prepare, Predictive models were built using;both the random forest and the support vector machine methods and were based on experimentally derived permeability data on 211 diverse compounds. The derived models were of similar predictive quality as compared to earlier published models but have the extra advantage of not only presenting a single predicted value for each, compound but also a reliable, individually assigned prediction range. The models use calculated descriptors and can quickly predict the skin permeation rate of new compounds.

Place, publisher, year, edition, pages
2017. Vol. 14, no 5, 1571-1576 p.
Keyword [en]
conformal prediction, skin penetration nonconformist, Scikit Learn, random forest, Support vector machines
National Category
Basic Medicine
Identifiers
URN: urn:nbn:se:uu:diva-323448DOI: 10.1021/acs.molpharmaceut.7b00007ISI: 000400633300024PubMedID: 28335598OAI: oai:DiVA.org:uu-323448DiVA: diva2:1119714
Available from: 2017-07-04 Created: 2017-07-04 Last updated: 2017-08-24Bibliographically approved
In thesis
1. Computational Modelling in Drug Discovery: Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening
Open this publication in new window or tab >>Computational Modelling in Drug Discovery: Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 47 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 235
Keyword
drug discovery, docking, virtual screening, tuberculosis, conformal prediction
National Category
Medicinal Chemistry
Identifiers
urn:nbn:se:uu:diva-328505 (URN)978-91-513-0049-8 (ISBN)
Public defence
2017-10-13, B/B42, Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2017-09-21 Created: 2017-08-24 Last updated: 2017-10-17

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Lindh, MartinKarlén, Anders

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