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User:Birulik/Insilico Medicine

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Insilico Medicine
Company typePrivate company
IndustryBiotechnology
Founded2014; 10 years ago (2014)
FounderAlex Zhavoronkov
Headquarters
Hong Kong
,
China
Area served
Worldwide
Key people
Alex Zhavoronkov
Number of employees
85 (2019)
DivisionsPharma.AI
Websitehttps://insilico.com

Insilico Medicine is an AI-focused biotechnology company, specializing in the application of deep learning, generative adversarial networks, and reinforcement learning for biological target discovery, biomarker development, drug discovery, analysis of clinical trials and other fields of digital medicine. The company’s main focus is on dermatological diseases, programs in oncology, fibrosis, Parkinson's disease, Alzheimer's disease, ALS, diabetes, sarcopenia and aging in general.[1] The company is headquartered in Hong Kong and operates 6 research and development facilities worldwide.[2][3]

History

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Insilico Medicine was founded by a computer scientist and biotechnologist Alex Zhavoronkov in 2014. The company started work in the field of drug discovery by using deep neural networks to identify potential drug targets by mining biological data. Insilico Medicine's research focus evolved in 2015 when Zhavoronkov applied Ian Goodfellow's works on machine learning, employing generative adversarial networks (GANs) in order to devise molecules with the desired medicinal chemistry properties de novo instead of screening existing databases of chemical compounds with the first peer-reviewed publication in GANs for drug discovery published in 2016.[4] Following initial research into this approach, Insilico pioneered the use of GANs and reinforcement learning in drug discovery, becoming increasingly attractive to investors from AI and biotechnology fields.[5][6][7][8] Since its establishment the company has raised over $50 million from venture funds and private longevity-focused investors, such as British philanthropist and entrepreneur Jim Mellon.[9] Insilico's largest $37 million Series B investment round in September 2019 was led by Qiming Venture Partners joined by a number of funds including Eight Roads Ventures, F-Prime Capital, Lilly Asia Ventures, Sinovation Ventures, Baidu Ventures, Pavilion Capital, BOLD Capital Partners, and Juvenescence.[10][11][12]

Recognition

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Insilico Medicine's AI-based approach to healthcare has brought it industry wide recognition. At the 2015 Palo Alto Personalized Medicine World Conference it was titled the most promising company[13] in the same year the company became Nvidia’s Emerging Company Summit finalist.[14] Furthermore in 2017 Nvidia listed Insilico Medicine among top 5 social impact AI companies and included Insilico into its Nvidia Inception program.[15] Insilico also found itself on CB Insights list of top ten private anti-aging companies in 2017 as well as the "A.I. 100" rating of most promising AI companies in 2018.[16] Finally, in 2018 the company was granted the Frost & Sullivan North American Technology Innovation Award for advances in aging research and drug development.[17] MIT Technology Review included AI-discovered molecules as one of ten breakthrough technologies in its annual review and highlighted Insilico Medicine's advances and key role in that field.[18][19] In 2020 the company was included in the Fierce 15 list of top biotechnology companies by Fierce Biotechnology.[20]

Research Areas

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Drug discovery

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Insilico Medicine pioneered the use of generative adversarial networks (GANs) and reinforcement learning (RL) in the field of drug discovery.[6] In contrary to traditional approaches, when medicinal chemists search for potential drug candidates in molecular libraries, Insilico's drug discovery engine is built upon AI imagination that devises novel molecules with intended properties based on past research and information about patented compounds with proven efficiency against specific biological targets.[21][22]

In 2016 Insilico published its first proof-of-concept for the use of deep learning algorithms to predict therapeutic use of medicinal chemicals based on information about other compounds.[23][24]. That year the company proposed the application of GANs, specifically the generative adversarial autoencoders in drug discovery. The team trained a form of GANs called the generative adversarial autoencoders (AAE) using molecular fingerprints of anti-cancer drugs to identify drug candidates in PubChem database.[25][26] Insilico's DruGAN (Drug + GAN) model, presented in 2017, introduced the transition from binary fingerprints to novel representation of molecules based on molecular graphs, thus allowing it to propose new molecular structures instead of matching output to molecular libraries.[27]

In 2019 Insilico Medicine and its research collaborators from the University of Toronto published a proof-of-concept paper on the use generative tensorial reinforcement learning (GENTRL) for drug discovery in Nature journal.[28] The researchers trained the generative model on the datasets of known inhibitors of DDR1, a tyrosine kinase target involved in progression of fibrosis, and tasked it to develop novel compounds that target DDR1. In 21 days the AI generated a large number of molecules that were put through sorting, scoring and review by chemists, with the most promising compound successfully passing in vitro and in vivo tests during the following 25 days. The team found the molecule to be potent against DDR1 and have drug-like qualities.[29] The publication of these results attracted attention and became 2nd most popular scientific paper in the Nature Index in September 2019.[30] The experiment was generally considered substantial advance in generative drug discovery.[31][32] This work was criticized by a team from Relay Therapeutics which pointed out that the only AI-generated compound tested in mice was similar to a known molecule.[33][34][35]

In late January to February 2020 Insilico Medicine used its machine learning methods to generate molecules that can be used in treatment of SARS-CoV-2 coronavirus. The company's AI was tasked to seek potential inhibitors of 3C-like protease, an enzyme critical for coronavirus reproduction, and was able to propose new molecules in 4 days. Insilico then published the research materials including molecular designs on its own website and ResearchGate, providing open access for researchers to examine and critique. Insilico Medicine promised to synthesize and test the most promising compounds and announced the search for industry collaborators to develop the following compounds.[36][37][38][39][40][41]

As of the beginning of 2020 Insilico Medicine also ran drug discovery programs for cancer, aging, fibrosis, Parkinson's disease, Alzheimer's disease, amyotrophic lateral sclerosis, diabetes and other conditions. One of the drug candidates developed by Insilico, the treatment for hair loss, is scheduled to start Phase 1 clinical trials.[12]

Biomarkers of Aging and Disease

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Insilico Medicine works on different biological aging clocks based on biomarkers in the blood, gut microbiome and other sources of data about an individual. The company uses deep learning models to find reliable predictors of biological age (correlated with health status and mortality, rather than chronological age which merely reflects the number of years one has lived) and to understand what healthy aging looks like. If validated, such models may be used by researchers to track response in future clinical trials of anti-aging treatments.[42]

Insilico's initial research into "hematologic aging clocks" began in the period 2015 to 2016 when the company trained a modular system of 21 deep neural networks with 60,000 samples from common blood biochemistry and cell count tests of relatively healthy individuals. The samples were linked to chronological age and sex. The researchers found the system capable of estimating a person's age within a time frame of 10 years with 83,5% accuracy and of determining a person's sex with 99% accuracy without measuring hormone levels.[43][44] The paper was published in Aging and became the second most popular publication in the journal's history.[45][46] That model was also used to power Aging.AI, an online platform that allowed individuals to determine their bioloigical age and sex by inputting their blood biochemistry data.[47] Future research by the firm included deep learning analysis of poplulation-specific samples belonging to Canadians, South Koreans and Eastern Europeans allowing Insilico to improve precision within a margin of 6 years.[48][49][50] Insilico Medicine also used its age-prediction model to analyze difference between the biochemical markers of smokers and non-smokers and quantify the acceleration of biological aging due to tobacco consumption, proposing a reliable deep learned method to determine an individual's smoking status.[51]

In 2019 Insilico Medicine proposed a "microbiome aging clock" based on deep learning analysis of an individual's gut microbiome, which is known to change throughout adulthood. Researchers gathered information about microbiomes of 1,165 Europeans, Asians and North Americans with roughly one-third of samples coming from populations in their 20s to 30s, 40s to 50s, and 60s to 90s respectively. Ninety percent of samples were tagged with age in order to train deep neural networks that were later tasked to guess the age of persons among the remaining ten percent of those sampled. As a result, Insilico's algorithms were able to predict an individual's age within a margin of 4 years. Insilico Medicine then suggested that this aging clock, if validated, could be combined with other biomarker predictors to provide a more precise picture of individual's health and biological age.[52][53][54][55]

Tools

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  • MOSES (acronym for molecular sets) is the benchmarking platform, launched by Insilico Medicine to facilitate collaboration in AI-driven drug discovery. It contains an unified molecular dataset based on ZINC database, several molecular generation models by Insilico, Neuromation and Alán Aspuru-Guzik's research group, and evaluation metrics.[28] MOSES aims to become an academic standard for generative models evaluation, comparison and sharing in drug discovery, accelerating the AI development in the industry the same way ImageNet has significantly boosted accuracy in image recognition[56][57] Insilico Medicine released the MOSES source code as open-source without restrictions or copyright ownership claims.[58]
  • Geroscope is Insilico Medicine's drug knowledge management system based on the data mining of compound databases, accessed through partnerships and collaborations. The algorithms aggregate huge amounts of genomic, transcriptomic, epigenetic, proteomic and clinical factors, as well as data on the effect the drugs play on gene expression in cells, cell lines and tissues to identify potential geroprotectors.[64]

Partnerships and Associations

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Insilico Medicine maintains over 150 academic and industry partnerships, focused on validation and application of its technologies in different areas, such as target identification, drug development, predictive analytics in biotech.[28] The company also relies on its partners to run clinical studies.[65] Its Notable industry collaborations included Pfizer,[66] Jiangsu Chia Tai Fenghai Pharmaceutical,[67] WiXu AppTec,[68] other big pharmaceutical companies,[69] TARA Biosystems,[70] Beijing Tide Pharmaceutical,[71][72] and others. Insilico's partners in academic field include the University of Copenhagen,[73] Gachon University, and Gil Medical Center in South Korea,[74] Oxford University,[75] Ageing Research at King's (a research center at King's College London) in United Kingdom.[17][76]

Insilico Medicine has formed two joint ventures with its strategic investor Juvenescence. The first one being Generait Pharmaceuticals (formerly named Juvenescence.AI), that works on clinical development of AI-generated molecules.[77] Insilico's second venture is Napa Therapeutics, established together with Buck Institute for Research on Aging and focused on development of compounds targeting NAD+ metabolism in aging-related diseases based on research by Eric M. Verdin.[78] Consortium.AI, the company established through a venture by Insilico and A2A Pharmaceuticals, focuses on development of highly selective treatment for duchenne muscular dystrophy using AI-powered drug discovery engine.[79] Another joint venture named Longenesis was founded by Insilico and blockchain mining and management company Bitfury Group to develop a blockchain solution for healthcare data management.[80][81]

In 2018 Insilico Medicine, as well as major pharmaceutical companies, technology developers and research organisations working on AI application in healthcare announced the creation of an industry-wide collaborative coalition named Alliance for Artificial Intelligence in Healthcare (AAIH).[82] The list of founding members included, but isn't limited to AWS, Bayer, GE Healthcare, GlaxoSmithKline, Johnson & Johnson, as well as University of Pittsburgh. Its first board meeting and official launch took place in San Francisco in January 2019, followed by an official statement by Alex Zhavoronkov.[83] The Alliance aimed to increase education outreach on AI, promote investments in AI R&D and develop policies and regulation in cooperation with governmental bodies in EU, US and beyond.[84] In October 2019 AIIH presented its first white paper "Artificial Intelligence in Healthcare: A Technical Primer" aimed to introduce concepts, standards and the potential of AI to broader healthcare community.[85]

Locations and management

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Insilico Medicine is headquartered in Hong Kong and employs 85 AI experts and scientist in research and development facilities in Belgium, Russia, South Korea, Taiwan, UK and the US. The company's recruitment strategy is focused on hackathons and competitions. For example, the company hosted Molhack II online hackathon in bioinformatics, biochemistry to support the establishment of its office in Taipei.[17] Insilico's founder and CEO is Alex Zhavoronkov. Insilico is active in research publications and collaborations: as of 2019 its research team published over 120 peer-reviewed papers with over 3300 citations.[28]

Notes

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  1. ^ Robin Seaton Jefferson (June 12, 2018). "AI And Biotech Companies In The East And West Invest In Combating Aging". Forbes. Retrieved March 16, 2020.
  2. ^ "Top Companies Using A.I. In Drug Discovery And Development". The Medical Futurist. September 17, 2019. Retrieved February 29, 2020.
  3. ^ Topol 2019, p. 2017.
  4. ^ Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, Alex Zhavoronkov (2016). "The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology". Oncotarget (in Russian). 8 (7) (Oncotarget ed.): 10883–10890. doi:10.18632/oncotarget.14073. ISSN 1949-2553. PMC 5355231. PMID 28029644.{{cite journal}}: CS1 maint: date and year (link) CS1 maint: multiple names: authors list (link)
  5. ^ Alex Knapp (September 2, 2019). "This Startup Used AI To Design A Drug In 21 Days". Forbes. Retrieved February 29, 2020.
  6. ^ a b Richard Staines. "Could AI create a brave new world of pharma R&D?". Deep Dive Digital Magazine. Retrieved February 29, 2020.
  7. ^ Hooper 2018, p. 223.
  8. ^ Drug Design 2019, p. 15.
  9. ^ Andrew McConaghie (April 11, 2017). "Billionaire Jim Mellon invests in anti-ageing research firm". Pharmaphorum. Retrieved February 29, 2020.
  10. ^ Carol Huang (September 10, 2019). "HK-based biotech startup gets venture capital injection". FinanceAsia. Retrieved February 29, 2020.
  11. ^ Amber Tong (September 10, 2019). "Alex Zhavoronkov follows landmark AI paper with $37M round for Insilico featuring top-notch China VCs". Endpoint News. Retrieved February 29, 2020.
  12. ^ a b Diamandis 2020, p. 165-166.
  13. ^ Nicola Bagalà (April 18, 2017). "Dr. Alex Zhavoronkov – A.I. Versus Aging". Life Extension Advocacy Foundation. Retrieved February 29, 2020.
  14. ^ Dean Takahashi (March 18, 2015). "Nvidia highlights five startups that use cool graphics technology". Venture Beat. Retrieved March 16, 2020.
  15. ^ Dean Takahashi (April 23, 2017). "Nvidia identifies the top 5 AI startups for social impact". VentureBeat. Retrieved February 29, 2020.
  16. ^ "Forever Young: 10 Anti-Aging Startups To Watch". CB Insights. May 2, 2017. Retrieved February 29, 2020.
  17. ^ a b c d "2018 North American Artificial Intelligence for Aging Research and Drug Development Technology Innovation Award" (PDF). Frost & Sullivan. Retrieved February 29, 2020.
  18. ^ "10 Breakthrough Technologies 2020". MIT Technology Review. February 26, 2020. Retrieved February 29, 2020.
  19. ^ "MIT Technology Review selects AI Molecular Design as a breakthrough of the year and highlights Insilico Medicine". Markets Insider. February 26, 2020. Retrieved February 29, 2020.
  20. ^ Conor Hale (March 9, 2020). "FierceMedTech's 2019 Fierce 15". Fierce Biotech. Retrieved March 16, 2020.
  21. ^ Charlotte Jee (September 3, 2019). "An AI system identified a potential new drug in just 46 days". MIT Technology Review. Retrieved February 29, 2020.
  22. ^ Danny Crichton (June 11, 2018). "With strategic investment, Insilico Medicine is using deep learning to defeat aging". TechCrunch. Retrieved February 29, 2020.
  23. ^ Yasmin Tayag (May 26, 2016). "How Artificial Intelligence Is Being Used to Discover New Uses for Drugs". Inverse. Retrieved February 29, 2020.
  24. ^ "Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data". Molecular Pharmaceutics. 13 (7): 2524–2530. 2016. doi:10.1021/acs.molpharmaceut.6b00248. PMC 4965264. PMID 27200455. {{cite journal}}: Unknown parameter |authors= ignored (help)
  25. ^ "Artificial intelligence to generate new cancer drugs on demand". EurekAlert!. December 22, 2016. Retrieved February 29, 2020.
  26. ^ "Scientists apply generative neural network to create new pharmaceutical medicines". News Medical. February 9, 2017. Retrieved February 29, 2020.
  27. ^ "Achieving high-value chemical diversity for the pharmaceutical artificial intelligence". EurekAlert!. October 13, 2017. Retrieved February 29, 2020.
  28. ^ a b c d "Intelligent drug discovery: Powered by AI" (PDF). Deloitte. 2019. Retrieved February 29, 2020.
  29. ^ Gregory Barber (September 2, 2019). "A Molecule Designed by AI Exhibits 'Druglike' Qualities". Wired. Retrieved February 29, 2020.
  30. ^ Bec Crew (October 1, 2019). "The 5 most popular scientific papers of September 2019". Nature Index. Retrieved February 29, 2020.
  31. ^ Sam Lemonick (September 4, 2019). "AI identifies drug candidate in weeks". C&EN. Retrieved February 29, 2020.
  32. ^ Derek Lowe (September 4, 2019). "Has AI Discovered a Drug Now? Guess". Science Translational Medicine. Retrieved February 29, 2020.
  33. ^ Alex Zhavoronkov, Alán Aspuru-Guzik (January 30, 2020). "Reply to 'Assessing the impact of generative AI on medicinal chemistry'". Nature Biotechnology. Retrieved March 16, 2020.
  34. ^ W. Patrick Walters, Mark Murcko (January 30, 2020). "Assessing the impact of generative AI on medicinal chemistry". Nature Biotechnology. Retrieved March 16, 2020.
  35. ^ Derek Lowe (February 4, 2020). "Arguing on AI Drug Discovery". Science Translational Medicine. Retrieved March 16, 2020.
  36. ^ Jeremy Kahn (February 6, 2020). "Startup uses A.I. to identify molecules that could fight coronavirus". Fortune. Retrieved February 29, 2020.
  37. ^ Sam Lemonick (February 4, 2020). "Two groups use artificial intelligence to find compounds that could fight the novel coronavirus". C&EN. Retrieved February 29, 2020.
  38. ^ Charlotte Harrison (February 27, 2020). "Coronavirus puts drug repurposing on the fast track". Nature Biotechnology. Retrieved February 29, 2020.
  39. ^ Jared Council, Brian Gormley (March 6, 2020). "Biotech Companies Tap AI to Speed Path to Coronavirus Treatments". Wall Street Journal. Retrieved March 9, 2020.
  40. ^ Stéphane Loignon (March 6, 2020). "Lutte contre le coronavirus : l'intelligence artificielle au service des scientifiques" (in French). Le Parisien. Retrieved March 9, 2020.
  41. ^ Megan Scudellari (March 18, 2020). "Five Companies Using AI to Fight Coronavirus". IEEE Spectrum. Retrieved March 20, 2020.
  42. ^ "Artificial Intelligence In Drug Discovery: Hope or Hype?" (PDF). C&EN. 2019. Retrieved February 29, 2020.
  43. ^ Katharine Sharpe (May 20, 2016). "Could a blood test reveal your AGE? AI computer can predict how old you by analysing chemicals from a sample". Mail Online. Retrieved February 29, 2020.
  44. ^ Sarah Kramer (February 19, 2016). "This medical company created an online calculator that uses AI to guess your age and sex". Business Insilder. Retrieved February 29, 2020.
  45. ^ "Deep biomarkers of human aging: Application of deep neural networks to biomarker development". Aging. 8 (5). Albany NY: 1021–1033. 2016. doi:10.18632/aging.100968. PMC 4931851. PMID 27191382. {{cite journal}}: Unknown parameter |authors= ignored (help)
  46. ^ Andrew McConaghie (April 11, 2017). "Billionaire Jim Mellon invests in anti-ageing research firm". Pharmaphorum. Retrieved February 29, 2020.
  47. ^ Jelor Gallego (May 23, 2016). "AI Uncovers the Biomarkers That Are Related to Aging". Futurism. Retrieved February 29, 2020.
  48. ^ Robby Berman (February 9, 2018). "This A.I. can predict how long you'll live—and it's free". Big Think. Retrieved February 29, 2020.
  49. ^ Anastasia Komarova (January 26, 2018). "Neural Networks Can Identify Any Person's Age". ITMO.News. Retrieved February 29, 2020.
  50. ^ Mellon 2017.
  51. ^ "Smoking Accelerates Biological Age, Says AI". Genetic Engineering & Biotechnology News. January 17, 2019. Retrieved February 29, 2020.
  52. ^ Emily Mullin (January 11, 2019). "The bacteria in your gut may reveal your true age". Science. Retrieved February 29, 2020.
  53. ^ Siobhán Dunphy (January 15, 2019). "Want to know someone's true age? Just look at their microbiome". European Scientist. Retrieved February 29, 2020.
  54. ^ Robin Marantz Henig (December 17, 2019). "How trillions of microbes affect every stage of our life—from birth to old age". National Geographic. Retrieved February 29, 2020.
  55. ^ Discovery 2019, p. 18.
  56. ^ "Insilico Medicine Announces MOSES Benchmark Platform for Molecular Generation". Synced. December 4, 2018. Retrieved February 29, 2020.
  57. ^ Andrii Buvalio (January 10, 2019). "2018: AI Is Surging In Drug Discovery Market". BiopharmaTrend.com. Retrieved February 29, 2020.
  58. ^ Terri Shieh-Newton (September 30, 2019). "Patenting Considerations for Artificial Intelligence in Biotech and Synthetic Biology". Mintz. Retrieved February 29, 2020.
  59. ^ Stephen Babcock (November 22, 2016). "Insilico Medicine's work on a 'virtual human' now has quantum computing behind it". Technical.ly. Retrieved February 29, 2020.
  60. ^ "How AI Will Help Us Defeat Aging". Wall Street Pit. April 24, 2017. Retrieved February 29, 2020.
  61. ^ "AI for Drug Discovery Landscape Overview (Industry Developments Q2 2018)" (PDF). Deep Knowledge Ventures. Retrieved February 29, 2020.
  62. ^ "Insilico Medicine научит нейросеть транскриптомному анализу" [Insilico Medicine Will Teach an Artificial Neural Network Transcriptome Analysis] (in Russian). Naked Science. November 16, 2016. Retrieved February 29, 2020.
  63. ^ "Метод iPANDA поможет в разработке персонального лечения" [iPanda Method Will Help In Development Of Personalized Treatment] (in Russian). Indicator. November 16, 2016. Retrieved February 29, 2020.
  64. ^ Ian C. Clift (October 28, 2014). "Betting On Big Data To Beat Aging". Seeking Alpha. Retrieved February 29, 2020.
  65. ^ Ruth Reader (September 20, 2019). "The billion-dollar race to change how drugs are made". Fast Company. Retrieved February 29, 2020.
  66. ^ Nick Paul Taylor (January 16, 2020). "Pfizer teams up with Insilico to mine data for drug targets". FierceBiotech. Retrieved February 29, 2020.
  67. ^ Conor Hale (October 9, 2019). "Insilico signs $200M AI drug discovery partnership with China's CTFH". FierceBiotech. Retrieved February 29, 2020.
  68. ^ Conor Hale (June 11, 2018). "WuXi AppTec to test AI-generated compounds from Insilico Medicine". FierceBiotech. Retrieved February 29, 2020.
  69. ^ Amirah Al Idrus (August 16, 2017). "GlaxoSmithKline taps Baltimore's Insilico for AI-based drug discovery". FierceBiotech. Retrieved February 29, 2020.
  70. ^ Danielle Brown (January 7, 2019). "Insilico Medicine, TARA Biosystems announce drug discovery partnership". AI in Healthcare. Retrieved February 29, 2020.
  71. ^ "Beijing Tide Pharma collaborates with Insilico for cancer therapy". BioSpectrumasia. March 18, 2020. Retrieved March 19, 2020.
  72. ^ "Insilico inks another deal with Sino Biopharma unit; Ramaswamy's Datavant to invest in the "patient journey"". EndPoints News. March 18, 2020. Retrieved March 20, 2020.
  73. ^ "Artificial intelligence is used to fight premature aging". University of Copenhagen. July 31, 2017. Retrieved February 29, 2020.
  74. ^ Marian Chu (April 12, 2019). "Insilico enters Korea to focus on skincare, wound-healing". Korea Biomedical Review. Retrieved February 29, 2020.
  75. ^ "Insilico to contribute to Oxford CDT research and educational program in AI". EurekAlert!. February 7, 2019. Retrieved March 16, 2020.
  76. ^ Anna Smith (May 23, 2019). "Insilico, ARK initiate health-span and longevity partnership". PharmaTimes. Retrieved February 29, 2020.
  77. ^ "Juvenescence Creates AI-Focused Approach to Longevity". Nanalyze. September 8, 2019. Retrieved February 29, 2020.
  78. ^ Conor Hale (August 14, 2018). "Insilico, Juvenescence and the Buck Institute form AI-based venture to tackle metabolism and aging-related diseases". FierceBiotech. Retrieved February 29, 2020.
  79. ^ Conor Hale (July 20, 2018). "Insilico and A2A launch new Duchenne-focused AI drug company". FierceBiotech. Retrieved February 29, 2020.
  80. ^ Gertrude Chavez-Dreyfuss (August 11, 2017). "U.S. blockchain company in tie-up on medical artificial intelligence". Reuters. Retrieved February 29, 2020.
  81. ^ Kyt Dotson (April 4, 2019). "Bitfury and Longenesis team up to build medical consent platform for research". Silicon Angle. Retrieved February 29, 2020.
  82. ^ "AI firms to establish research platform". AuntMinnie. September 13, 2018. Retrieved February 29, 2020.
  83. ^ Danielle Brown (January 3, 2019). "Newly-formed alliance for AI in healthcare to officially launch". AI in Healthcare. Retrieved February 29, 2020.
  84. ^ Jabe Wilson (June 26, 2019). "Healthcare Organizations, Including Major Pharmas, Band Together to Form AI-focused Group". Elsevier. Retrieved February 29, 2020.
  85. ^ "AAIH releases primer to advance understanding within the industry". EurekAlert!. October 17, 2019. Retrieved February 29, 2020.

References

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  • Topol, Eric (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books. p. 217. ISBN 9781541644649.
  • "The Future of Drugs". The Future Is Faster Than You Think. New York: Simon & Schuster. 2019. pp. 165–166. ISBN 9781982109684. {{cite book}}: Unknown parameter |authors= ignored (help)
  • "Is ageing a desease?". Juvenescence: Investing in the age of longevity. Douglas: Fruitfull Publication. 2017. ISBN 978-0-9930478-1-7. {{cite book}}: Unknown parameter |authors= ignored (help)
  • "Longevity". Superhuman: Life at the Extremes of Our Capacity. New York: Simon & Schuster. 2018. p. 223. ISBN 978-1-5011-6871-0. {{cite book}}: Unknown parameter |authors= ignored (help)
  • AI identifies kinase drug candidate in weeks. But can it do the same for harder targets?. Discovery Report. 2019. p. 18. {{cite book}}: Unknown parameter |authors= ignored (help)
  • The rise of AI drug discovery disruptors. Case Study 3. Insilico Medicine and AI Imagination For Drug Design. Diegem: Deloitte.Insights. 2019. p. 15. {{cite book}}: Unknown parameter |authors= ignored (help)
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