{"id":1827,"date":"2018-03-06T15:20:57","date_gmt":"2018-03-06T15:20:57","guid":{"rendered":"http:\/\/ukqsar.org\/?p=1827"},"modified":"2018-03-14T13:44:32","modified_gmt":"2018-03-14T13:44:32","slug":"posters-spring-2018","status":"publish","type":"post","link":"https:\/\/ukqsar.org\/index.php\/2018\/03\/06\/posters-spring-2018\/","title":{"rendered":"Posters Cardiff Spring 2018"},"content":{"rendered":"

Poster Presentations<\/u><\/strong><\/p>\n

Open or Download Poster Abstracts – use RightClick\/SaveAs<\/a><\/p>\n

    \n
  1. \u201cMondrian Conformal Prediction \u2013 Confidence Predictions with Excellent Handling of Imbalanced Data\u201d; Fredrik Svensson<\/strong>, University of Cambridge and IOTA Pharmaceuticals<\/li>\n
  2. \u201cPredicting Ligand-Protein binding affinities in Leucyl-tRNA Synthetases\u201d; Grace Edmund<\/strong>, Oxford Drug Design<\/li>\n
  3. \u201cActive search for computer-aided drug design\u201d; Steven Oatley<\/strong>, University of Nottingham<\/li>\n
  4. \u201cAugmenting De novo Drug Design using Reaction Classification\u201d; Gianmarco Ghiandoni<\/strong>, University of Sheffield<\/li>\n
  5. \u201cMachine Learning: Predicting Chemical Ames Mutagenicity\u201d; Charmaine Chu<\/strong>, University of Liverpool<\/li>\n
  6. \u201cOn the mechanisms of DNA G-quadruplexes targeting by gold(I) N-heterocyclic compounds: new insights by combined meta-dynamics and biophysical methods\u201d; Darren Wragg<\/strong>, University of Cardiff<\/li>\n
  7. \u201cUsing advanced computational methods to model the binding of flexible proteins: a case study from the coagulation cascade\u201d; Martin Rosellen<\/strong>, UCL<\/li>\n
  8. \u201cWhichP450 \u2013 A Multi-class Categorical Model to Predict the Major Metabolising CYP450 Isoform for a Compound\u201d; Peter Hunt<\/strong>, Optibrium Ltd.<\/li>\n
  9. \u201cWater Networks and Topological Data Analysis: A Data Science Approach to Understanding Intermolecular Structures\u201d; Lee Steinberg<\/strong>, University of Southampton<\/li>\n
  10. \u201cComparison of multitask prediction methods for chemical data\u201d; Antonio de la Vega de Leon<\/strong>, University of Sheffield<\/li>\n
  11. \u201cInvestigating the importance of solid state descriptors for statistical models of temperature dependent aqueous solubility\u201d; Richard Marchese Robinson<\/strong>, University of Leeds<\/li>\n
  12. \u201cMeta-QSAR: a large-scale application of meta-learning to drug design and discovery\u201d; Ivan Olier<\/strong>, Liverpool John Moores University<\/li>\n
  13. \u201cDL_ANALYSER Notation for Atomic Interactions (DANAI): A Natural Annotation System for Molecular Interactions, Using Carboxylic Acids as Test Cases\u201d; Chin Yong<\/strong>, STFC Daresbury Laboratory and University of Manchester<\/li>\n
  14. \u201cG-protein coupled receptor-KNIME Automated Modelling Platform (GPCR_KAMP)\u201d; Ajay Pal<\/strong>, Dublin Institute of Technology<\/li>\n
  15. \u201cFrom exploration to exploitation: how do QSAR models depict trends during lead optimisation?\u201d; Chris Luscombe and David Marcus<\/strong>, Glaxo SmithKline<\/li>\n
  16. \u201cAdaptation of a Matched Molecular Pair Identification Algorithm for Solid State Informatics Analysis of the Cambridge Structural Database\u201d; Jakub Janowiak<\/strong>, University of Leeds<\/li>\n
  17. \u201cThe mechanism of aquaporin inhibition by gold compounds elucidated by biophysical and computational methods\u201d; Andreia de Almeida<\/strong>, University of Cardiff<\/li>\n
  18. \u201cRegression conformal prediction of ADME data using error models for normalisation\u201d; Christina Founti<\/strong>, University of Sheffield<\/li>\n
  19. \u201cApplication of quantum mechanics and\/or structural fingerprinting for the prediction of glutathione conjugation site-specificity?\u201d; Martin Payne<\/strong>, LHASA Ltd.<\/li>\n
  20. \u201cConformational variation from a spectral geometry perspective\u201d; Matthew Seddon<\/strong>, University of Sheffield<\/li>\n
  21. \u201cLarge-scale comparison between robust methods for QSAR\u201d; Phillippa McCabe<\/strong>, Liverpool John Moores University<\/li>\n
  22. \u201cModelling ChEMBL protein targets using conformal prediction\u201d; Nicolas Bosc<\/strong>, EBI<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Poster Presentations Open or Download Poster Abstracts – use RightClick\/SaveAs \u201cMondrian Conformal Prediction \u2013 Confidence Predictions with Excellent Handling of<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[4],"tags":[],"class_list":["post-1827","post","type-post","status-publish","format-standard","hentry","category-meetings"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t