{"id":58,"date":"2019-08-14T09:49:23","date_gmt":"2019-08-14T09:49:23","guid":{"rendered":"http:\/\/www.cironpharma.com\/blog\/?p=58"},"modified":"2019-09-25T06:59:28","modified_gmt":"2019-09-25T06:59:28","slug":"a-melody-called-machine-learning","status":"publish","type":"post","link":"https:\/\/www.cironpharma.com\/blog\/a-melody-called-machine-learning\/","title":{"rendered":"A Melody Called Machine Learning"},"content":{"rendered":"\n<p>The\npipeline to new drugs isn\u2019t straightforward. Whilst some estimates of only\n5-10% of drug programs making it to approval may be lowballing the true rate,\nsuccess is still far from guaranteed. What is guaranteed is the gargantuan time\nand cost of taking a compound from discovery to therapy. In response, pharmas\nare wising up and seeking to make use of what is perhaps their greatest\nresource \u2013 data. <\/p>\n\n\n\n<p>Leveraging\nmolecular data before trials begin means better selection of target compounds\nand a quicker development process. But data isn\u2019t just a tool, it\u2019s a\ncommodity, and a hugely valuable one. How can we encourage companies to share\ndata assets freely?<\/p>\n\n\n\n<p>One\npotential solution is offered by Machine Learning Ledger Orchestration for Drug\nDiscovery (MELLODDY). This Innovative Medicines Initiative (IMI)-funded\nconsortium hopes to leverage blockchain architecture to guarantee shareability\nand security for pharmaceutical data. What\u2019s more, it already has the backing\nof 10 major pharmas. We talked to Hugo Ceulemans, MELLODDY Project Leader and\nScientific Director, Discovery Data Sciences at Janssen Pharmaceutical NV and\nMathieu Galtier, Project Coordinator at data science company Owkin to find out\nmore. <\/p>\n\n\n\n<p>MELLODDY\nis a new Innovative Medicines Initiative (IMI)-funded consortium of\npharmaceutical, technology and academic partners across Europe.&nbsp; MELLODDY is aiming, for the first time, to\nuse machine learning methods on the annotated chemical collections of 10 pharma\ncompanies to develop a platform capable of creating more accurate prediction\nmodels of which compounds could be promising in the later stages of drug\ndiscovery and development.<\/p>\n\n\n\n<p>MELLODDY\nwas launched to accelerate drug discovery using machine learning to unlock the\nmaximum potential of pharma data \u2013 the world\u2019s largest collection of small\nmolecules with known biochemical or cellular activity \u2013 to enable more accurate\npredictive models and increase efficiencies in drug discovery.<\/p>\n\n\n\n<p>Maximizing the chances of discovering a drug successfully with machine learning is a great leap forward in the ever exciting world of medicinal pharmaceuticals. This is indeed a great news for <a href=\"http:\/\/www.cironpharma.com\/contact.asp\"><strong>pharma manufacturers in India<\/strong><\/a> and other <a href=\"http:\/\/www.cironpharma.com\/blog\/behind-pharma-doors-how-formulations-are-made\/\"><strong>formulation companies in India<\/strong><\/a>. The technology can be a boost in many imaginable and also unimaginable ways. <\/p>\n\n\n\n<p><strong>Read more here<\/strong>:&nbsp; https:\/\/www.technologynetworks.com\/drug-discovery\/blog\/maximizing-drug-discovery-success-with-machine-learning-322056 <\/p>\n","protected":false},"excerpt":{"rendered":"<p>The pipeline to new drugs isn\u2019t straightforward. Whilst some estimates of only 5-10% of drug programs making it to approval may be lowballing the true rate, success is still far from guaranteed. What is guaranteed is the gargantuan time and cost of taking a compound from discovery to therapy. In response, pharmas are wising up<\/p>\n","protected":false},"author":1,"featured_media":59,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/posts\/58"}],"collection":[{"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/comments?post=58"}],"version-history":[{"count":2,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/posts\/58\/revisions"}],"predecessor-version":[{"id":61,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/posts\/58\/revisions\/61"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/media\/59"}],"wp:attachment":[{"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/media?parent=58"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/categories?post=58"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cironpharma.com\/blog\/wp-json\/wp\/v2\/tags?post=58"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}