{"id":3611,"date":"2025-01-29T10:56:39","date_gmt":"2025-01-29T10:56:39","guid":{"rendered":"https:\/\/23547798491.thesite.link\/cbs\/pu-computing-vathia-mathisi-dl-stin-ereyna-anaptyxi-neon-farmakeytikon-proionton\/"},"modified":"2025-01-30T09:57:21","modified_gmt":"2025-01-30T09:57:21","slug":"pu-computing-vathia-mathisi-dl-stin-ereyna-anaptyxi-neon-farmakeytikon-proionton","status":"publish","type":"post","link":"https:\/\/www.cbs.gr\/en\/pu-computing-vathia-mathisi-dl-stin-ereyna-anaptyxi-neon-farmakeytikon-proionton\/","title":{"rendered":"PU Computing &amp; Deep Learning (DL) in the Research &amp; Development of new Pharmaceutical Products"},"content":{"rendered":"<figure class=\"wp-block-post-featured-image\"><img loading=\"lazy\" decoding=\"async\" width=\"672\" height=\"372\" src=\"https:\/\/www.cbs.gr\/wp-content\/uploads\/2025\/01\/Screenshot_29-1-2025_12568_www.cbs_.gr_-672x372.jpeg\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"\" style=\"object-fit:cover;\" \/><\/figure>\n\n\n<p>The <strong>deep learning<\/strong> (<a href=\"https:\/\/www.techtarget.com\/searchenterpriseai\/definition\/deep-learning-deep-neural-network\" target=\"_blank\" rel=\"noreferrer noopener\">DL<\/a>) has been linked to fields in <strong>various scientific areas<\/strong>, serving as a <strong>primary tool in pharmaceutical chemistry and pharmacology<\/strong> which are associated with <strong>drug discovery and drug development<\/strong>.<\/p>\n\n\n\n<p>This application is highlighted in:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>using modern experimental graphics processing units (<a href=\"https:\/\/researchcomputing.princeton.edu\/support\/knowledge-base\/gpu-computing\" target=\"_blank\" rel=\"noreferrer noopener\">GPU<\/a>)<\/li>\n\n\n\n<li>the development of algorithms with the capabilities of GPU.<\/li>\n<\/ul>\n\n\n\n<p>The fields of bioinformatics, cheminformatics, and medical informatics, rely on <a href=\"https:\/\/www.frontiersin.org\/articles\/10.3389\/fddsv.2021.728551\/full\" target=\"_blank\" rel=\"noreferrer noopener\">Computer-Aided Drug Discovery (CADD)<\/a>, utilizing Deep Learning methods enhanced with GPUs. The pioneering applications of <a href=\"https:\/\/www.pharmatutor.org\/articles\/a-review-on-computer-aided-drug-design-and-its-implications-in-drug-discovery-and-development-process\" target=\"_blank\" rel=\"noreferrer noopener\">CADD<\/a> emphasize its contribution to solving problems related to statistics and modeling. However, mechanistic learning is still under development to offer substantial benefits.<\/p>\n\n\n\n<p>In 2007, NVIDIA introduced the <a href=\"https:\/\/en.wikipedia.org\/wiki\/CUDA\" target=\"_blank\" rel=\"noreferrer noopener\">Compute Unified Device Architecture (CUDA)<\/a> transforming the role of <strong>the computational power of GPUs from<\/strong> <strong>CPU<\/strong> to <strong>GPU<\/strong>.<\/p>\n\n\n\n<p>In September 2014, NVIDIA introduced cuDNN, a GPU \u2013 accelerated library with functions for use in <a href=\"https:\/\/www.kdnuggets.com\/2020\/02\/deep-neural-networks.html\" target=\"_blank\" rel=\"noreferrer noopener\">deep neural networks (DNN)<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"324\" height=\"439\" src=\"https:\/\/www.cbs.gr\/wp-content\/uploads\/2025\/01\/image-152.png\" alt=\"\" class=\"wp-image-3300\" srcset=\"https:\/\/www.cbs.gr\/wp-content\/uploads\/2025\/01\/image-152.png 324w, https:\/\/www.cbs.gr\/wp-content\/uploads\/2025\/01\/image-152-221x300.png 221w\" sizes=\"auto, (max-width: 324px) 100vw, 324px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Explanation of Deep Learning Models in <\/strong><strong>GPU<\/strong><\/h4>\n\n\n\n<p>The <strong>GPU<\/strong><strong>&#8211;<\/strong><strong>centered<\/strong> <strong>deep learning models<\/strong>, the core concept, is based on <strong>systematic reductions in the computational costs of models<\/strong> with respect to algorithms that run on CPU. At the same time, <strong>high-performance deep learning models<\/strong> make numerous improvements with <strong>developing the <\/strong><strong>GPU<\/strong>.<\/p>\n\n\n\n<p>Recently, GPU algorithms have had the ability to adapt to the needs of ecosystem operators in the energy sector. As a result, <strong>deep learning models<\/strong> are expected to be <strong>an important part of future biomedical phenomena<\/strong>, highlighting the impact of AI and quantum computing and <strong>improving parametric chromatographic methods<\/strong>.<\/p>\n\n\n\n<p>The combination <strong>of the continuous and uninterrupted application of formalisms is made possible<\/strong> by the broad application of <strong>DL<\/strong> <strong>models and <\/strong><strong>GPU<\/strong> <strong>parallel processing<\/strong>. Based on the hardware selection, <strong>DL<\/strong> <strong>is combined<\/strong> with the ability to solve problems by applying <strong>economic optimization and analysis<\/strong> based on the model of the <em>prospect theory of risk assessment<\/em> (QSAR) as the <strong>scheduled production of formal models<\/strong>.<\/p>\n\n\n\n<p>Recently, there have been <strong>hybrid methods of technical knowledge<\/strong> where the terms combine synthetic deep learning models. The <strong>result is a large and accurate selection of chemical compounds<\/strong> with the help of experimental methods that produce data sets in the development of virtual libraries.<\/p>\n\n\n\n<p>More generally, the <strong>use of <\/strong><strong>Big<\/strong> <strong>Data<\/strong> enables the broad use of DL algorithms. With the automated application of the genetic algorithms and the high screening efficiency of <a href=\"https:\/\/en.wikipedia.org\/wiki\/High-throughput_screening\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>High<\/strong><strong>&#8211;<\/strong><strong>throughput<\/strong> <strong>screening(HTS)<\/strong><\/a><strong>, huge amounts of data are processed.<\/strong><\/p>\n\n\n\n<p>The use of and the application of AI models is a milestone for chromatographic and preclinical diagnostics. The automation of this diagnostic method with the help of the Technical Knowledge and the broad and with accurate experimental application with many data of DL models and GPU parallel processing gives new prospects in the continuous application of new excellent models.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The deep learning (DL) has been linked to fields in various scientific areas, serving as a primary tool in pharmaceutical chemistry and pharmacology which are associated with drug discovery and drug development.<\/p>\n","protected":false},"author":2,"featured_media":3299,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-3611","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>PU Computing &amp; 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