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dc.contributor.authorYevseyeva, Irynaen
dc.contributor.authorLenselink, Eelke B.en
dc.contributor.authorVries, Alicedeen
dc.contributor.authorIJzerman, Adriaan P.en
dc.contributor.authorDeutz, A.en
dc.contributor.authorEmmerich, Michael T. M.en
dc.date.accessioned2018-10-08T14:13:30Z
dc.date.available2018-10-08T14:13:30Z
dc.date.issued2018-09-28
dc.identifier.citationYevseyeva I., Lenselink E.B., de Vries A., IJzerman A.P., Back T., A.H. Deutz, Emmerich M.T.M. (2019) Application of portfolio optimization to drug discovery. Information Sciences, 475, pp. 29-43en
dc.identifier.urihttp://hdl.handle.net/2086/16696
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractIn this work, a problem of selecting a subset of molecules, which are potential lead candidates for drug discovery, is considered. Such molecule subset selection problem is formulated as a portfolio optimization, well known and studied in financial management. The financial return, more precisely the return rate, is interpreted as return rate from a potential lead and calculated as a product of gain and probability of success (probability that a selected molecule becomes a lead), which is related to performance of the molecule, in particular, its (bio-)activity. The risk is associated with not finding active molecules and is related to the level of diversity of the molecules selected in portfolio. It is due to potential of some molecules to contribute to the diversity of the set of molecules selected in portfolio and hence decreasing risk of portfolio as a whole. Even though such molecules considered in isolation look inefficient, they are located in sparsely sampled regions of chemical space and are different from more promising molecules. One way of computing diversity of a set is associated with a covariance matrix, and here it is represented by the Solow-Polasky measure. Several formulations of molecule portfolio optimization are considered taking into account the limited budget provided for buying molecules and the fixed size of the portfolio. The proposed approach is tested in experimental settings for three molecules datasets using exact and/or evolutionary approaches. The results obtained for these datasets look promising and encouraging for application of the proposed portfolio-based approach for molecule subset selection in real settings.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectPortfolio approachen
dc.subjectMulticriteria optimizationen
dc.subjectDecision supporten
dc.subjectDrug discoveryen
dc.titleApplication of portfolio optimization to drug discoveryen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.ins.2018.09.049
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-09-28en
dc.researchinstituteCyber Technology Institute (CTI)en


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