YEARS

2013-2015

AUTHORS

Larisa Soldatova

TITLE

Learning to learn how to design drugs

ABSTRACT

A key step in developing a new drug is to learn quantitative structure activity relationships (QSARs). These are mathematical functions that predict how well chemical compounds will act as drugs. QSARs are used to guide the synthesis of new drugs. The current situation is: 1) There is a vast range of approaches to learning QSARs. 2) It is clear from theory and practice that the best QSAR approach depends on the type of problem. 3) Currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. We therefore propose to make a step-change in QSAR research. We will utilise newly available public domain chemoinformatic databases, and in-house datasets, to systematically run extensive comparative QSAR experiments. We will then generalise these results to learn which target-type/ compound-type/ compound-representation /learning-method combinations work best together. We do not propose to develop any new QSAR method. Rather, we will learn how to better apply existing QSAR methods. This approach is called "meta-learning", using machine learning to learn about QSAR leaning. We will make the knowledge we learn publically available to guide and improve future QSAR learning.

FUNDED PUBLICATIONS

  • Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases.
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    20 TRIPLES      17 PREDICATES      21 URIs      10 LITERALS

    Subject Predicate Object
    1 grants:caf43eb8d719a89d4121c660168b422f sg:abstract A key step in developing a new drug is to learn quantitative structure activity relationships (QSARs). These are mathematical functions that predict how well chemical compounds will act as drugs. QSARs are used to guide the synthesis of new drugs. The current situation is: 1) There is a vast range of approaches to learning QSARs. 2) It is clear from theory and practice that the best QSAR approach depends on the type of problem. 3) Currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. We therefore propose to make a step-change in QSAR research. We will utilise newly available public domain chemoinformatic databases, and in-house datasets, to systematically run extensive comparative QSAR experiments. We will then generalise these results to learn which target-type/ compound-type/ compound-representation /learning-method combinations work best together. We do not propose to develop any new QSAR method. Rather, we will learn how to better apply existing QSAR methods. This approach is called "meta-learning", using machine learning to learn about QSAR leaning. We will make the knowledge we learn publically available to guide and improve future QSAR learning.
    2 sg:endYear 2015
    3 sg:fundingAmount 173171.0
    4 sg:fundingCurrency GBP
    5 sg:hasContribution contributions:f700f489024cd763ca1b821f5de88cd3
    6 sg:hasFieldOfResearchCode anzsrc-for:17
    7 anzsrc-for:1702
    8 sg:hasFundedPublication articles:188684aa2b0efc365a4f886a3a236494
    9 articles:2c9d6fcae3a11e1cbb8629916862f1e3
    10 sg:hasFundingOrganization grid-institutes:grid.421091.f
    11 sg:hasRecipientOrganization grid-institutes:grid.7728.a
    12 sg:language English
    13 sg:license http://scigraph.springernature.com/explorer/license/
    14 Contains UK public sector information licensed under the Open Government Licence v2.0 (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/2/).
    15 sg:scigraphId caf43eb8d719a89d4121c660168b422f
    16 sg:startYear 2013
    17 sg:title Learning to learn how to design drugs
    18 sg:webpage http://gtr.rcuk.ac.uk/project/255BBB61-5D94-429A-B98D-F6E10E907599
    19 rdf:type sg:Grant
    20 rdfs:label Grant: Learning to learn how to design drugs
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