{"id":159,"date":"2017-10-25T00:52:44","date_gmt":"2017-10-24T22:52:44","guid":{"rendered":"https:\/\/fhedin.com\/?page_id=159"},"modified":"2017-10-25T01:26:22","modified_gmt":"2017-10-24T23:26:22","slug":"deep-neural-network-learning-of-physicochemical-properties","status":"publish","type":"page","link":"https:\/\/fhedin.com\/?page_id=159","title":{"rendered":"Deep Neural Network learning of physicochemical properties"},"content":{"rendered":"<p><span data-reactid=\"24\"><span class=\"text-with-line-breaks\" data-reactid=\"27\"><span class=\"Linkify\">The aim is to learn physicochemical properties of interest from simple structural representations, using deep neural networks: in particular I would like to investigate how much can be learned from molecular fingerprints generated by using the SMILES molecular representation.<\/span><\/span><\/span><\/p>\n<figure style=\"width: 848px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" id=\"yui_3_14_1_1_1508887463482_1331\" src=\"https:\/\/www.researchgate.net\/profile\/Dong-Sheng_Cao\/publication\/235919348\/figure\/fig1\/AS:299748008448007@1448476902809\/Figure-1-Representation-of-a-molecular-substructure-fingerprint-with-a-substructure.png\" alt=\"\" width=\"848\" height=\"575\" \/><figcaption class=\"wp-caption-text\">Representation of a molecular substructure fingerprint with a substructure fingerprint dictionary of given substructure patterns. This molecule is represented in a series of binary bits that represent the presence or absence of particular substructures in the molecules. SOURCE : https:\/\/www.researchgate.net\/publication\/235919348_manual_for_chemopy\/figures<\/figcaption><\/figure>\n<p>This project was initiated in 0ctober 2017 during the IPAM&#8217;s long program &#8220;Complex High-Dimensional Energy Landscapes&#8221;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The aim is to learn physicochemical properties of interest from simple structural representations, using deep neural networks: in particular I would like to investigate how much can be learned from molecular fingerprints generated by using the SMILES molecular representation. This project was initiated in 0ctober 2017 during the IPAM&#8217;s long program &#8220;Complex High-Dimensional Energy Landscapes&#8221;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":155,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-159","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/pages\/159","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fhedin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=159"}],"version-history":[{"count":2,"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/pages\/159\/revisions"}],"predecessor-version":[{"id":176,"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/pages\/159\/revisions\/176"}],"up":[{"embeddable":true,"href":"https:\/\/fhedin.com\/index.php?rest_route=\/wp\/v2\/pages\/155"}],"wp:attachment":[{"href":"https:\/\/fhedin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=159"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}