{"id":8885,"date":"2013-02-05T00:22:39","date_gmt":"2013-02-04T22:22:39","guid":{"rendered":"http:\/\/hgpu.org\/?p=8885"},"modified":"2013-02-05T00:22:39","modified_gmt":"2013-02-04T22:22:39","slug":"gpu-accelerated-high-accuracy-molecular-docking-using-guided-differential-evolution","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8885","title":{"rendered":"GPU-Accelerated High-Accuracy Molecular Docking using Guided Differential Evolution"},"content":{"rendered":"<p>The objective in molecular docking is to determine the best binding mode of two molecules in silico. A common application of molecular docking is in drug discovery where a large number of ligands are docked into a protein to identify potential drug candidates. This is a computationally intensive problem especially if the flexibility of the molecules is taken into account. We show how MolDock, which is a high accuracy method for flexible molecular docking using a variant of differential evolution, can be parallelised on both CPU and GPU. The methods presented for parallelising the workload result in an average speedup of 3.9x on a 4-core CPU and 27.4x on a comparable CUDA enabled GPU when docking 133 ligands of different sizes. Furthermore, the presented parallelisation schemes are generally applicable and can easily be adapted to other flexible docking methods.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The objective in molecular docking is to determine the best binding mode of two molecules in silico. A common application of molecular docking is in drug discovery where a large number of ligands are docked into a protein to identify potential drug candidates. This is a computationally intensive problem especially if the flexibility of the [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,89,38,3],"tags":[1781,14,616,1788,20,226,558],"class_list":["post-8885","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-medicine","category-paper","tag-biology","tag-cuda","tag-differential-evolution","tag-medicine","tag-nvidia","tag-nvidia-geforce-8800-gt","tag-pharmaceuticals"],"views":2883,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8885","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8885"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8885\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8885"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8885"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8885"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}