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Predicting off-target binding profiles with confidence using Conformal Prediction
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Pharmaceutical Bioinformatics)ORCID-id: 0000-0001-6740-9212
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Pharmaceutical Bioinformatics)
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Pharmaceutical Bioinformatics)ORCID-id: 0000-0001-6709-7116
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Pharmaceutical Bioinformatics)
Vise andre og tillknytning
2018 (engelsk)Inngår i: Frontiers in Pharmacology, ISSN 1663-9812, E-ISSN 1663-9812, Vol. 9, artikkel-id 1256Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only providing point predictions. We here describe the use of conformal prediction for predicting off-target interactions with models trained on data from 31 targets in the ExCAPE dataset, selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence p-values for each class. The full pre-processing and model training process is openly available as scientific workflows on GitHub, rendering it fully reproducible. We illustrate the usefulness of the methodology on a set of compounds extracted from DrugBank. The resulting models are published online and are available via a graphical web interface and an OpenAPI interface for programmatic access.

sted, utgiver, år, opplag, sider
2018. Vol. 9, artikkel-id 1256
Emneord [en]
target profiles, predictive modelling, conformal prediction, machine learning, off-target, adverse effects
HSV kategori
Forskningsprogram
Farmakologi
Identifikatorer
URN: urn:nbn:se:uu:diva-357894DOI: 10.3389/fphar.2018.01256ISI: 000449322200002PubMedID: 30459617OAI: oai:DiVA.org:uu-357894DiVA, id: diva2:1240587
Forskningsfinansiär
EU, Horizon 2020, 731075Tilgjengelig fra: 2018-08-21 Laget: 2018-08-21 Sist oppdatert: 2019-01-15bibliografisk kontrollert
Inngår i avhandling
1. Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web
Åpne denne publikasjonen i ny fane eller vindu >>Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2018. s. 68
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 256
Emneord
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
HSV kategori
Forskningsprogram
Bioinformatik; Farmakologi
Identifikatorer
urn:nbn:se:uu:diva-358353 (URN)978-91-513-0427-4 (ISBN)
Disputas
2018-09-28, Room B22, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
EU, Horizon 2020, 654241Swedish e‐Science Research CentereSSENCE - An eScience Collaboration
Tilgjengelig fra: 2018-09-04 Laget: 2018-08-28 Sist oppdatert: 2018-09-10

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Lampa, SamuelAlvarsson, JonathanArvidsson Mc Shane, StaffanBerg, ArvidSpjuth, Ola
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