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dc.contributor.author
Awale, Mahendra
dc.contributor.author
Riniker, Sereina
dc.contributor.author
Kramer, Christian
dc.date.accessioned
2021-07-06T11:43:29Z
dc.date.available
2021-07-06T11:43:29Z
dc.date.issued
2020-06-22
dc.identifier.issn
1549-9596
dc.identifier.issn
0095-2338
dc.identifier.issn
1520-5142
dc.identifier.other
10.1021/acs.jcim.0c00269
dc.identifier.uri
http://hdl.handle.net/20.500.11850/493275
dc.description.abstract
Generation and prioritization of new molecules are the most central part of the drug design process. Matched molecular series analysis (MMSA) has recently been proposed as a formal approach that captures both of these key elements of design. In order to better understand the power of MMSA and its specific limitations, we here evaluate its performance as an ADME property prediction tool. We use four large and diverse inhouse data sets, logD, microsomal clearance, CYP2C9, and CYP3A4 inhibition. MMSA follows the concept of parallel structure–activity relationship (SAR), where if two identical substituent series on different scaffolds show similarity in their property profiles, SAR from one series can be transferred to the other series. We test four different similarity metrics to identify pairs of molecular series where information can be transferred. We find that the best prediction performance is achieved by a combination of centered root-mean-square deviation (cRMSD) and a network score approach previously published by Keefer et al. However, cRMSD alone strikes the best balance between accuracy and the number of predictions that can be made. We identify statistical metrics that allow estimating when MMSA predictions will work, similar to the well-known applicability domain concept in machine learning. MMSA achieves a prediction accuracy that is comparable to a standard machine-learning model and matched molecular pair analysis. In contrast to machine learning, however, it is very easy to understand where MMSA predictions are coming from. Finally, to prospectively test the power of MMSA, we retested compounds that were strong outliers in the initial predictions and show how the MMSA model can help to identify erroneous data points. © American Chemical Society 2020
en_US
dc.language.iso
en
en_US
dc.publisher
American Chemical Society
en_US
dc.title
Matched Molecular Series Analysis for ADME Property Prediction
dc.type
Journal Article
dc.date.published
2020-05-05
ethz.journal.title
Journal of Chemical Information and Modeling
ethz.journal.volume
60
ethz.journal.issue
6
ethz.journal.abbreviated
J. Chem. Inf. Model.
ethz.pages.start
2903
ethz.pages.end
2914
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02515 - Laboratorium für Physikalische Chemie / Laboratory of Physical Chemistry::09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02515 - Laboratorium für Physikalische Chemie / Laboratory of Physical Chemistry::09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
ethz.date.deposited
2020-07-09T05:25:10Z
ethz.source
WOS
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-07-06T11:43:38Z
ethz.rosetta.lastUpdated
2022-03-29T10:18:08Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/437207
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/425387
ethz.COinS
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