{"id":6106,"date":"2018-01-28T15:28:17","date_gmt":"2018-01-28T20:28:17","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/benevolentai-revolutionizing-drug-discovery-using-artificial-intelligence\/"},"modified":"2018-01-28T15:30:51","modified_gmt":"2018-01-28T20:30:51","slug":"benevolentai-revolutionizing-drug-discovery-using-artificial-intelligence","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/benevolentai-revolutionizing-drug-discovery-using-artificial-intelligence\/","title":{"rendered":"BenevolentAI: Revolutionizing drug discovery using Artificial Intelligence"},"content":{"rendered":"

Developing biopharmaceuticals (drugs) is expensive and time-consuming.\u00a0 For example, developing a drug in 2010 took 10 years[i]<\/sup><\/a> and cost $2.6B[ii]<\/sup><\/a>, 145% more than 10 years earlier.\u00a0 This is because in the traditional drug discovery process, chemists sift through millions of compounds of which only a fraction are approved[iii]<\/sup><\/a> – for every 25,000 compounds that start in the laboratory, 25 are tested in humans, and only 5 make it to market.[iv]<\/sup><\/a><\/p>\n

\"\"<\/a><\/p>\n

Source: PhRMa\u2019s 2015 \u201cBiopharmaceutical Research & Development\u201d PDF report<\/p>\n

However, advances in deep learning and artificial intelligence (AI), availability of big data, and cheaper computing power are revolutionizing the drug discovery process.\u00a0 They can make the cost of discovering drugs 50% cheaper[v]<\/sup><\/a> and reduce the time it takes from identifying a target disease to finding a molecule that acts against from 5.5 years to just a year.[vi]<\/sup><\/a><\/p>\n

A trailblazer in this space is BenevolentAI, valued at $1.7B within five years of launch.[vii]<\/a> BenevolentAI uses AI to mine and analyze vast amounts already-published biomedical information, from clinical trials data to academic papers. \u00a0The company\u2019s director, Jackie Hunter believes that “There\u2019s a new paper out every 30 seconds. This is a huge amount of information that isn\u2019t being used for discovery and development of new drugs.\u201d [viii]<\/a> Moreover, for researchers, assimilating this data and identifying meaningful hypotheses from it would be extremely challenging.\u00a0 To solve that, an architectural innovation is necessary, and that is BenevolentAI\u2019s value proposition \u2013 instead of trying to discover a new compound from scratch, BenevolentAI uses its AI system to try to find new drug candidates from existing information and new potential uses for existing drug candidates.<\/p>\n

Thus, there are two ways in which BenevolentAI creates value.\u00a0 One, it identifies molecules that have failed in clinical trials and predicts how those same compounds can instead be more efficient in targeting other diseases.\u00a0 Second, it uses the predictive power of its AI algorithms to design new molecules, and extract new hypotheses based on over a billion relationships between genes, targets, diseases, proteins, and drugs.[ix]<\/a>\u00a0 Both these propositions make the drug discovery process more cost and time-efficient, creating significant value for the biopharmaceutical industry and the healthcare ecosystem.<\/p>\n

Not only is BenevolentAI creating value, but it is also capturing some of it.\u00a0 First, BenevolentAI has 24 drug candidates in just four years \u2013 significantly faster than traditional drug discovery.\u00a0 Second, BenevolentAI signed an $800 million deal in 2014 to hand over two Alzheimer drug targets to an unnamed US company for development and will take a cut from the profits if its drugs are developed and sold.[x]<\/a>\u00a0 Finally, last year BenevolentAI reached a deal to license the right to develop, manufacture and commercialize a number of novel clinical stage drug candidates from J&J.[xi]<\/a><\/p>\n

BenevolentAI\u2019s ability to create and capture value, as described above, will continue to grow rapidly with better deep learning algorithms, robust bioinformatics databases, and higher computing power.\u00a0 Consequently, it (and its peers) may altogether replace the drug discovery teams of traditional biopharma companies, as has been foreshadowed by several collaborations in 2016-17 between AI-based drug discovery startups such as Exscientia, Numerate, and Insilico Medicine, and traditional biopharma companies such as Pfizer, Genentech, and GSK.[xii]<\/a><\/p>\n

That is what makes BenevolentAI a winner.<\/p>\n

 <\/p>\n

References:<\/p>\n

[i]<\/sup><\/a> Tufts CSDD, \u201cBriefing: Cost of Developing a New Drug,\u201d http:\/\/csdd.tufts.edu\/files\/uploads\/Tufts_CSDD_briefing_on_RD_cost_study_-_ Nov_18,_2014..pdf, Nov 2014.<\/p>\n

[ii]<\/sup><\/a> Tufts Center for the Study of Drug Development (CSDD), November 2014, https:\/\/cen.acs.org\/articles\/92\/web\/2014\/11\/Tufts-Study-Finds-Big-Rise.html<\/a>, accessed 28 Jan 2018<\/p>\n

[iii]<\/sup><\/a> Drug Discovery AI to Scour a Universe of Molecules for Wonder Drugs, Jason Dorrier, Nov 19, 2017, https:\/\/singularityhub.com\/2017\/11\/19\/drug-discovery-ai-to-scour-a-universe-of-molecules-for-wonder-drugs\/#sm.0000khfzfifpzdaawtr29uu7s96ox<\/a>, Singularity Hub, accessed 28 Jan 2018<\/p>\n

[iv]<\/sup><\/a> Drug development: the journey of a medicine from lab to shelf, Ingrid Torjesen, May 12, 2015, http:\/\/www.pharmaceutical-journal.com\/publications\/tomorrows-pharmacist\/drug-development-the-journey-of-a-medicine-from-lab-to-shelf\/20068196.article<\/a>, The Pharmaceutical Journal, accessed 28 Jan 2018<\/p>\n

[v]<\/sup><\/a> How AI Is Transforming Drug Creation, Daniela Hernandez, Jun 25, 2017, https:\/\/www.wsj.com\/articles\/how-ai-is-transforming-drug-creation-1498442760<\/a>, Wall Street Journal, accessed 28 Jan 2018<\/p>\n

[vi]<\/sup><\/a> Big pharma turns to AI to speed drug discovery, GSK signs deal, Reuters Staff, Jul 3, 2017, https:\/\/www.reuters.com\/article\/pharmaceuticals-ai-gsk\/rpt-big-pharma-turns-to-ai-to-speed-drug-discovery-gsk-signs-deal-idUSL8N1JR2HL<\/a>, Market News, accessed 28 Jan 2018<\/p>\n

[vii]<\/a> London-Based AI Startup on Hiring Spree Amid U.K. Boom, Giles Turner, Jun 14, 2017, https:\/\/www.bloomberg.com\/news\/articles\/2017-06-14\/london-based-ai-startup-on-hiring-spree-as-u-k-boom-continues<\/a>, Bloomberg Technology, accessed 28 Jan 2018<\/p>\n

[viii]<\/a> This AI unicorn is disrupting the pharma industry in a big way, Joao Medeiros, 31 Aug 2017, http:\/\/www.wired.co.uk\/article\/benevolent-ai-london-unicorn-pharma-startup<\/a>, WIRED, accessed 27 Jan 2018<\/p>\n

[ix]<\/a> This AI unicorn is disrupting the pharma industry in a big way, Joao Medeiros, 31 Aug 2017, http:\/\/www.wired.co.uk\/article\/benevolent-ai-london-unicorn-pharma-startup<\/a>, WIRED, accessed 27 Jan 2018<\/p>\n

[x]<\/a> A British tech unicorn is trying to cure Alzheimer’s and ALS with artificial intelligence, Shona Ghosh, Apr 24, 2017, http:\/\/www.businessinsider.com\/benevolent-ai-ceo-ken-mulvany-interview-2017-4<\/a>, Business Insider, accessed 28 Jan 2018<\/p>\n

[xi]<\/a> Machine Learning Drug Discovery Applications \u2013 Pfizer, Roche, GSK, and More, Jon Walker, Jan 11, 2018, https:\/\/www.techemergence.com\/machine-learning-drug-discovery-applications-pfizer-roche-gsk\/<\/a>, techemergence, accessed 28 Jan 2018<\/p>\n

[xii]<\/a> Machine Learning Drug Discovery Applications \u2013 Pfizer, Roche, GSK, and More, Jon Walker, Jan 11, 2018, https:\/\/www.techemergence.com\/machine-learning-drug-discovery-applications-pfizer-roche-gsk\/<\/a>, techemergence, accessed 28 Jan 2018<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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