دانلود مقاله انگلیسی رایگان:یادگیری ماشین و رویکردهای مبتنی بر هوش مصنوعی برای کشف لیگاند زیست فعال و تشخیص GPCR-لیگاند - 2020
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  • Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
    Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition


    ترجمه فارسی عنوان مقاله:

    یادگیری ماشین و رویکردهای مبتنی بر هوش مصنوعی برای کشف لیگاند زیست فعال و تشخیص GPCR-لیگاند


    منبع:

    Sciencedirect - Elsevier - Methods,Corrected proof,doi:10.1016/j.ymeth.2020.06.016


    نویسنده:

    Sebastian Raschkaa,⁎,1, Benjamin Kaufmanb


    چکیده انگلیسی:

    In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent stateof- the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
    Keywords: Molecular representations | GPCR ligands | Drug discovery | Deep learning | Machine learning | Graph convolutional neural networks


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 22
    حجم فایل: 1581 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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