Non-Linear Assessment of Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives

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Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives</title> <meta name="description" content="Non-Linear Assessment of Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives"> <meta name="keywords" content="artificial neural networks, liquid chromatography, molecular descriptors, steroids, sum of ranking differences"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg 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class="card-body"><strong>Paper Count:</strong> 87360</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Non-Linear Assessment of Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milica%20Karadzic">Milica Karadzic</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevric"> Lidija Jevric</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac-Kuzmanovic"> Sanja Podunavac-Kuzmanovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kovacevic"> Strahinja Kovacevic</a>, <a href="https://publications.waset.org/abstracts/search?q=Anamarija%20Mandic"> Anamarija Mandic</a>, <a href="https://publications.waset.org/abstracts/search?q=Katarina%20Penov%20Gasi"> Katarina Penov Gasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Marija%20Sakac"> Marija Sakac</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandar%20Okljesa"> Aleksandar Okljesa</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Nikolic"> Andrea Nikolic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology). <iframe src="https://publications.waset.org/abstracts/50587.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=liquid%20chromatography" title=" liquid chromatography"> liquid chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=steroids" title=" steroids"> steroids</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20of%20ranking%20differences" title=" sum of ranking differences"> sum of ranking differences</a> </p> <a href="https://publications.waset.org/abstracts/50587/non-linear-assessment-of-chromatographic-lipophilicity-and-model-ranking-of-newly-synthesized-steroid-derivatives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50587.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th 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Non-Linear Assessment of Chromatographic Lipophilicity and Model Ranking of Newly Synthesized Steroid Derivatives