{"id":506868,"date":"2020-11-16T09:07:00","date_gmt":"2020-11-16T09:07:00","guid":{"rendered":"https:\/\/www.capgemini.com\/?post_type=client-story&#038;p=514831"},"modified":"2023-10-26T06:49:10","modified_gmt":"2023-10-26T06:49:10","slug":"integrating-artificial-intelligence-ai-into-the-drug-discovery-phase-of-pharmaceutical-rd","status":"publish","type":"client-story","link":"https:\/\/www.capgemini.com\/fi-en\/news\/client-stories\/integrating-artificial-intelligence-ai-into-the-drug-discovery-phase-of-pharmaceutical-rd\/","title":{"rendered":"Integrating artificial intelligence (AI) into the drug discovery phase of pharmaceutical R&#038;D"},"content":{"rendered":"\n<header class=\"wp-block-cg-blocks-hero-reusable is-style-default header heroReusable  \"><div class=\"header-bgs\"><picture><source srcset=\"https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=2880&amp;quality=70 1x, https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=2880&amp;quality=70 2x\" media=\"(min-width: 1500px)\"\/><source srcset=\"https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=1440&amp;quality=70 1x, https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=2880&amp;quality=70 2x\" media=\"(min-width: 992px)\"\/><source srcset=\"https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=1024&amp;quality=70 1x, https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=1024&amp;quality=70 2x\" media=\"(min-width: 768px)\"\/><source srcset=\"https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=768&amp;quality=70 1x, https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg?w=768&amp;quality=70 2x\" media=\"(min-width: 0)\"\/><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2020\/11\/ÎÚÑ»´«Ã½_Client-Stories_AI-in-drug-discovery.jpg\" class=\"header-img header-img-d\" alt=\"\" style=\"object-fit:cover;object-position:39% 22%\" loading=\"eager\"\/><\/picture><\/div><div class=\"heroPictureCardHeaderShape\"><\/div><div class=\"container\"><div class=\"row\"><div class=\"col-md-12\"><div class=\"box\"><div class=\"boxTagWrapper\"><div class=\"tagInfo\"><div><span class=\"box-tag\">Client story<\/span><\/div><\/div><\/div><div class=\"box-title\"><h1 data-maxlength=\"34\">Integrating artificial intelligence (AI) into the drug discovery phase of pharmaceutical R&amp;D<\/h1><\/div><div class=\"inner-row-insight download-btn\"><div class=\"col-md-4 downloadFiles addPadding\"><a class=\"button-download--small\" type=\"download\" href=\"https:\/\/www.capgemini.com\/no-no\/wp-content\/uploads\/2021\/03\/Pharmaceutical-case-study-2.pdf\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><span>Download client story<\/span><span class=\"type\">3 MB  <\/span><\/a><\/div><\/div><\/div><div class=\"infoWrapper \"><div class=\"row\"><div class=\"col-md-4\"><span class=\"fieldName\">Client: A large, multinational biopharmaceutical company <\/span><\/div><div class=\"col-md-4\"><span class=\"fieldName\">Region: UK<\/span><\/div><div class=\"col-md-4\"><span class=\"fieldName\">Industry: Life Sciences<\/span><\/div><\/div><\/div><\/div><\/div><\/div><\/header>\n\n\n\n<section class=\"wp-block-cg-blocks-intro-para undefined section section--intro\"><div class=\"intro-para\"><div class=\"container\"><div class=\"row\"><div class=\"col-12 col-md-1\"><nav class=\"article-social\"><ul class=\"social-nav\"><li class=\"ip-order-fb\"><a href=\"https:\/\/www.facebook.com\/sharer\/sharer.php?u=https:\/\/www.capgemini.com\/no-no\/news\/client-stories\/integrating-artificial-intelligence-ai-into-the-drug-discovery-phase-of-pharmaceutical-rd\/\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-fb\"><\/i><span class=\"sr-only\">Facebook<\/span><\/a><\/li><li class=\"ip-order-tw\"><a href=\"https:\/\/twitter.com\/intent\/tweet?url=https:\/\/www.capgemini.com\/no-no\/news\/client-stories\/integrating-artificial-intelligence-ai-into-the-drug-discovery-phase-of-pharmaceutical-rd\/&amp;text=Integrating artificial intelligence (AI) into the drug discovery phase of pharmaceutical R&amp;D\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-tw\"><\/i><span class=\"sr-only\">Twitter<\/span><\/a><\/li><li class=\"ip-order-li\"><a href=\"https:\/\/www.linkedin.com\/sharing\/share-offsite\/?url=https:\/\/www.capgemini.com\/no-no\/news\/client-stories\/integrating-artificial-intelligence-ai-into-the-drug-discovery-phase-of-pharmaceutical-rd\/&amp;text=Integrating artificial intelligence (AI) into the drug discovery phase of pharmaceutical R&amp;D\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-li\"><\/i><span class=\"sr-only\">Linkedin<\/span><\/a><\/li><\/ul><\/nav><\/div><div class=\"col-12 col-md-11 col-lg-10\"><h2 class=\"intro-para-title\">ÎÚÑ»´«Ã½ brings a blend of data science, IT and science capabilities to unravel the high complexity in pharmaceutical R&amp;D to enable more intelligent, value-driven innovation<\/h2><p class=\"intro-para-content\"><strong>Client Challenge:<\/strong> Like all companies in the healthcare industry, this global pharmaceutical giant was under tremendous pressure to reduce the time, cost and complexity involved in developing new drugs.<br><br><strong>Solution:<\/strong> Via a longstanding strategic partnership with ÎÚÑ»´«Ã½ focusing on data science, through a variety of different yet connected projects the pharmaceutical company is increasingly integrating AI and machine learning into the discovery phase of drug development, to transform the process<br><br><strong>Benefits:<\/strong><br>-Increase effectiveness of drugs discovered, and value from the drug discovery process<br>-Remove cost, time and inefficiencies from the drug discovery process<br>-Provide research teams\u2019 intuitive access to innovative technologies that maximize their scientific expertise<\/p><\/div><\/div><\/div><\/div><\/section>\n\n\n\n<section class=\"wp-block-cg-blocks-group undefined section section--article-content section--story-content article-body\"><div class=\"article-main-content\"><div class=\"container\"><div class=\"row\"><div class=\"col-12 col-md-11 col-lg-10 offset-md-1 offset-lg-1\"><div class=\"article-text article-quote-text\">\n<h3 class=\"wp-block-heading\" id=\"h-the-need-for-speed\">The need for speed<\/h3>\n\n\n\n<p>The COVID-19 pandemic has shone a spotlight on the need for faster research and development (R&amp;D) processes in life sciences. At the moment, bringing a new drug to market costs at least $2 billion and can take up to 10 years, and the pharmaceutical industry is all too aware that drug discovery, trials and therapy launches are too complex, costly and slow: Life threatening viral diseases like COVID-19 and Ebola, have piled on the pressure to change this.<\/p>\n\n\n\n<p>One of the world\u2019s large pharmaceutical companies wanted to use data science and AI to improve the route to market for new drug therapies. With the entire industry in the throes of transformation, and experiencing a turbulent market environment, the race was on to find new drugs and new indications for existing ones.<\/p>\n\n\n\n<p>In order to ensure that it had access to the right tools to create value across the company\u2019s portfolio and development pipeline, the pharmaceutical client called upon its existing partnership with ÎÚÑ»´«Ã½ to accelerate its shift towards faster and more connected AI-augmented drug discovery processes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-advancing-discovery\">Advancing discovery<\/h3>\n\n\n\n<p>The lead discovery phase of drug development involves working to find candidate molecules that will be effective in treating a disease, a complex process involving vast scales of potential data: the number of small molecule candidates available for drug design is up to 10^60. The pharmaceutical company worked with ÎÚÑ»´«Ã½ to create and apply new machine learning methods to improve \u2018cheminformatics\u2019: the computational and mathematical techniques involved in analysing collections of molecules and their properties. By combining broad AI expertise with deep subject matter knowledge, it was possible to apply techniques from seemingly unrelated areas to solve pharmaceutical R&amp;D challenges in computational drug modelling.<\/p>\n\n\n\n<p>One such project involved harnessing the neural networks behind cutting-edge language translation methods to help establish new molecules with the potential to have the right drug-like properties and biological activity. Working together, the organization and ÎÚÑ»´«Ã½ were able to apply language translation techniques to \u2018translate\u2019 the outline of an existing molecule into a new one to generate candidates that match requirements. This <a href=\"https:\/\/www.drugtargetreview.com\/article\/47713\/applying-ai-language-translation-to-the-creation-of-new-pharmaceuticals\/\" target=\"_blank\" rel=\"noreferrer noopener\">ground-breaking approach<\/a> uses machine learning techniques to guide the design and refinement of these molecules.<\/p>\n\n\n\n<p>A second project focused on the simplification and acceleration of research workflow. Inconsistent interfaces and varying data requirements between the many different tools for the computer simulation of the chemical space of molecules, and their interactions with proteins, can make it hard for the scientific community to use and maintain the associated technologies. To solve this problem, the pharmaceutical company and ÎÚÑ»´«Ã½ created a system that combines the entire spectrum of tools, for generating molecules, making predictions and designing experiments. This enables researchers to combine their scientific expertise with AI techniques without needing deep technical knowledge of the system itself.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-an-intelligent-future\">An intelligent future<\/h3>\n\n\n\n<p>These are just two examples of how the pharmaceutical company and ÎÚÑ»´«Ã½ have used AI to improve efficiencies, address complexity and more quickly innovate to create value. This includes not only looking for new molecular solutions but also for the potential to adapt existing drugs for new uses. Using AI for predictive modelling at the early stages of drug development allows researchers to examine more molecular variations while accelerating prioritization. And this has the potential to transform drug discovery: increasing the effectiveness of what is developed, and cutting cost, time and inefficiencies from the entire process. In doing so, the partnership is demonstrating that AI provides a more effective and trustworthy means of obtaining more value from complex data at scale.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"<p>ÎÚÑ»´«Ã½ brings a blend of data science, IT and science capabilities to unravel the high complexity in pharmaceutical 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