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  1. Article ; Online: Krein support vector machine classification of antimicrobial peptides.

    Redshaw, Joseph / Ting, Darren S J / Brown, Alex / Hirst, Jonathan D / Gärtner, Thomas

    Digital discovery

    2023  Volume 2, Issue 2, Page(s) 502–511

    Abstract: Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow ... ...

    Abstract Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid
    Language English
    Publishing date 2023-02-27
    Publishing country England
    Document type Journal Article
    ISSN 2635-098X
    ISSN (online) 2635-098X
    DOI 10.1039/d3dd00004d
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Kernel Methods for Predicting Yields of Chemical Reactions.

    Haywood, Alexe L / Redshaw, Joseph / Hanson-Heine, Magnus W D / Taylor, Adam / Brown, Alex / Mason, Andrew M / Gärtner, Thomas / Hirst, Jonathan D

    Journal of chemical information and modeling

    2021  Volume 62, Issue 9, Page(s) 2077–2092

    Abstract: The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in ... ...

    Abstract The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reactivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models outperformed the quantum chemical SVR models, along the dimension of each reaction component. The applicability of the models was assessed with respect to similarity to training. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalizability of the models, with particular interest along the aryl halide dimension.
    MeSH term(s) Machine Learning ; Prospective Studies
    Language English
    Publishing date 2021-10-26
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.1c00699
    Database MEDical Literature Analysis and Retrieval System OnLINE

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