Automated experiment design for drug development

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dc.contributor.authorEriksson, Hannes
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:44:01Z
dc.date.available2019-07-03T13:44:01Z
dc.date.issued2015
dc.description.abstractTesting drugs in discovery is time consuming and expensive. An idea is then to eliminate unpromising compounds from the testing phase by using online learning methods to predict properties of yet to be tested compounds and determining which drugs to test. This is done by comparing substructures in the graph representation of compounds, transformed into a compressed high dimensional space where a Gaussian process bandit and a linear bandit is used to predict properties of new compounds. Results show that the bandits perform signi cantly better than random selection and that the feature compression probably does not decrease the overall accuracy of the predictions.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/219466
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectInformations- och kommunikationsteknik
dc.subjectComputer and Information Science
dc.subjectInformation & Communication Technology
dc.titleAutomated experiment design for drug development
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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