دانلود مقاله انگلیسی رایگان:جوانب مثبت و منفی غربالگری مجازی مبتنی بر داده های بزرگ  عمومی: در استخراج سیلیکون برای مهارکننده های برمودومین جدید - 2019
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  • Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors
    Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors


    ترجمه فارسی عنوان مقاله:

    جوانب مثبت و منفی غربالگری مجازی مبتنی بر "داده های بزرگ " عمومی: در استخراج سیلیکون برای مهارکننده های برمودومین جدید


    منبع:

    Sciencedirect - Elsevier - European Journal of Medicinal Chemistry, 165 (2019) 258-272: doi:10:1016/j:ejmech:2019:01:010


    نویسنده:

    Iuri Casciuc a, Dragos Horvath a, Anastasiia Gryniukova b, Kateryna A. Tolmachova c, d, Oleksandr V. Vasylchenko c, Petro Borysko b, Yurii S. Moroz e, f, Jürgen Bajorath g, Alexandre Varnek


    چکیده انگلیسی:

    The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain “Big Data” for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure- activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6- fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.
    Keywords: Bromodomain BRD4 binders | Generative topographic mapping | Virtual screening | Classification models | Ligand-based pharmacophores | Docking


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 15
    حجم فایل: 1918 کیلوبایت

    قیمت: رایگان


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