1 Ocak 2019 Dergi makalesi Açık Erişim

Rifaioglu, Ahmet Sureyya; Atas, Heval; Martin, Maria Jesus; Cetin-Atalay, Rengul; Atalay, Volkan; Dogan, Tunca

Dublin Core

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  <dc:creator>Rifaioglu, Ahmet Sureyya</dc:creator>
  <dc:creator>Atas, Heval</dc:creator>
  <dc:creator>Martin, Maria Jesus</dc:creator>
  <dc:creator>Cetin-Atalay, Rengul</dc:creator>
  <dc:creator>Atalay, Volkan</dc:creator>
  <dc:creator>Dogan, Tunca</dc:creator>
  <dc:date>2019-01-01</dc:date>
  <dc:description>The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/75391</dc:identifier>
  <dc:identifier>oai:zenodo.org:75391</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>BRIEFINGS IN BIOINFORMATICS 20(5) 1878-1912</dc:source>
  <dc:title>Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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