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A confidence predictor for logD using conformal regression and a support-vector machine
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)ORCID iD: 0000-0002-0122-6680
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)ORCID iD: 0000-0001-6709-7116
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Spjuth)ORCID iD: 0000-0001-6740-9212
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)
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2018 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, no 1, article id 17Article in journal (Refereed) Published
Abstract [en]

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Place, publisher, year, edition, pages
2018. Vol. 10, no 1, article id 17
Keywords [en]
Conformal prediction, LogD, Machine learning, QSAR, RDF, Support-vector machine
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-347779DOI: 10.1186/s13321-018-0271-1ISI: 000429065900001PubMedID: 29616425OAI: oai:DiVA.org:uu-347779DiVA, id: diva2:1195839
Funder
EU, Horizon 2020, 731075Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-08-28Bibliographically approved
In thesis
1. 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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Lapins, MarisLampa, SamuelBerg, ArvidSchaal, WesleyAlvarsson, JonathanSpjuth, Ola

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Lapins, MarisArvidsson, StaffanLampa, SamuelBerg, ArvidSchaal, WesleyAlvarsson, JonathanSpjuth, Ola
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Journal of Cheminformatics
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