1 Ocak 2019 Dergi makalesi Açık Erişim

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

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        "name": "Rifaioglu, Ahmet Sureyya"
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        "affiliation": "Middle East Tech Univ, Grad Sch Informat, Canc Syst Biol Lab CanSyL, TR-06800 Ankara, Turkey", 
        "name": "Atas, Heval"
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      {
        "affiliation": "European Mol Biol Lab, European Bioinformat Inst, Cambridge, England", 
        "name": "Martin, Maria Jesus"
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      {
        "affiliation": "Middle East Tech Univ, Dept Comp Engn, TR-06800 Ankara, Turkey", 
        "name": "Cetin-Atalay, Rengul"
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      {
        "affiliation": "Middle East Tech Univ, Dept Comp Engn, TR-06800 Ankara, Turkey", 
        "name": "Atalay, Volkan"
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    "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.", 
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      "issue": "5", 
      "pages": "1878-1912", 
      "title": "BRIEFINGS IN BIOINFORMATICS", 
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    "title": "Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases"
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