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Deep Learning in Drug Discovery and Diagnostics, 2017 - 2035

INTRODUCTION
Deep learning is a novel machine learning technique that can be used to generate relevant insights from large volumes of data. The term Deep Learning was coined in 2006 by Geoffrey Hinton to refer to algorithms that enable computers to analyze objects and text in videos and images. Fundamentally, deep learning algorithms are designed to analyze and use large volumes of data to improve the capabilities of machines. Companies, such as Google, Amazon, Facebook, LinkedIn, IBM and Netflix, are already using deep learning algorithms to analyze users’ activities and make customized suggestions and recommendations based on individual preferences. Today, in many ways, deep learning algorithms have enabled computers to see, read and write. In light of recent advances, the error rate associated with machines being able to analyze and interpret medical images has come down to 6%, which, some research groups claim, is even better than humans.

The applications of the technology are being explored across a variety of areas. Specifically in healthcare, the American Recovery and Reinvestment Act of 2009 and the Precision Medicine Initiative of 2015 have widely endorsed the value of medical data in healthcare. Owing to several such initiatives, medical big data is expected to grow approximately 50-fold to reach 25,000 petabytes by 2020. Since 80% of this is unstructured, it is difficult to generate valuable / meaningful insights using conventional data mining techniques. In such cases, deep learning has emerged as a novel solution. Lead identification and optimization in drug discovery, support in patient recruitment for clinical trials, medical image analysis, biomarker identification, drug efficacy analysis, drug adherence evaluation, sequencing data analysis, virtual screening, molecule profiling, metabolomic data analysis, EMR analysis and medical device data evaluation are examples of applications where deep learning based solutions are being explored.

The likely benefits associated with the use of deep learning based solutions in the above mentioned areas is estimated to be worth multi billion dollars. There are well-known references where deep learning models have accelerated the drug discovery process and provided solutions to precision medicine. With potential applications in drug repurposing and preclinical research, deep learning in drug discovery is likely to have great opportunity. In diagnostics, an increase in the speed of diagnosis is likely to have a profound impact in regions with large patient to physician ratios. The implementation of such solutions is anticipated to increase the efficiency of physicians providing a certain amount of relief to the overly-burdened global healthcare system.

SCOPE OF THE REPORT
The “Deep Learning: Drug Discovery and Diagnostics Market, 2017-2035” report examines the current landscape and future outlook of the growing market of deep learning solutions within the healthcare domain. Primarily driven by the big data revolution, deep learning algorithms have emerged as a novel solution to generate relevant insights from medical data. This continuing shift towards digitalization of healthcare system has been backed by a number of initiatives taken by the government, and has also sparked the interest of several industry / non-industry players. The involvement of global technology companies and their increasing collaborations with research institutes and hospitals are indicative of the research intensity in this field. At the same time, the pharma giants have been highly active in adopting the digital models. Companies such as AstraZeneca, Pfizer and Novartis continue to evaluate the digital health initiatives across drug discovery, clinical trial management and medical diagnosis. Some notable examples of such digital health initiatives include GSK and Pfizer’s collaboration with Apple for the use of the latter’s research kit in clinical trials, Biogen’s partnership with Fitbit for using smart wearables in clinical trial management, and Teva Pharmaceuticals’ partnership with American Well to use Smart Inhalers for patients with asthma and COPD.

Backed by funding from several Venture Capital firms and strategic investors, deep learning has emerged as one of the most widely explored initiatives within digital healthcare. The current generation of deep learning models are flexible and have the ability to evolve and become more efficient over time. Despite being a relatively novel field of research, these models have already demonstrated significant potential in the healthcare industry.

One of the key objectives of this study was to identify the various deep learning solutions that are currently available / being developed to cater to unmet medical needs, and also evaluate the future prospects of deep learning within the healthcare industry. These solutions are anticipated to open up significant opportunities in the field of drug discovery and diagnostics as the healthcare industry gradually shifts towards digital solutions. In addition to other elements, the study covers the following:
 The current status of the market with respect to key players, specific applications and the therapeutic areas in which these solutions can be applied.
 The various initiatives that are being undertaken by technology giants, such as IBM, Google, Facebook, Microsoft, NVIDIA and Samsung. The presence of these stakeholders signifies the opportunity and the impact that these solutions are likely to have in the near future. Specifically, we have presented a comparative analysis of the deep learning solutions developed by IBM and Google.
 Detailed profiles of some of the established, as well as emerging players in the industry, highlighting key technology features, primary applications and other relevant information.
 The impact of venture capital funding in this area. It is important to mention that since the industry has witnessed the emergence of several start-ups, funding is a key enabler that is likely to drive both innovation and product development in the coming years.
 An elaborate valuation analysis of companies that are involved in applying deep learning in drug discovery and diagnostics. We built a multi-variable dependent valuation model to estimate the current valuation of a number of companies focused in this domain.
 Future growth opportunities and likely impact of deep learning in the drug discovery and diagnostics domains. The forecast model, backed by robust secondary research and credible inputs from primary research, was primarily based on the likely time-saving and its associated cost-saving opportunity to the healthcare system.

For the purpose of the study, we invited over 100 stakeholders to participate in a survey to solicit their opinions on upcoming opportunities and challenges that must be considered for a more inclusive growth. Our opinions and insights presented in this study were influenced by discussions conducted with several key players in this domain. The report features detailed transcripts of interviews held with Mausumi Acharya (CEO, Advenio Technosys), Carla Leibowitz (Head of Strategy and Marketing, Arterys) and Deekshith Marla (CTO, Arya.ai).

EXAMPLE HIGHLIGHTS
1. During our research, we identified close to 100 industry / non-industry players that are exploring their proprietary deep learning based technologies in drug discovery and diagnostics. A majority of these companies (61%) were founded post 2010. In fact, between 2013 and 2016 alone, the industry saw the emergence of 50 startups in this field.
2. More than 55% of the companies working in this space are applying their deep learning models for diagnostic purposes. Of these, 78% of the companies offer solutions for medical imaging analysis. Notable examples include (in alphabetical order) Arterys, AvalonAI, Bay Labs, Behold.ai, Butterfly Network, CAMELOT biomedical systems, Cyrcadia Health, Enlitic, iCarbonX, Lunit and Zebra Medical Vision.
3. On the other hand, close to 35% of the companies engaged in this domain are focused on applying deep learning models in drug discovery. 57% of these companies provide deep learning powered drug discovery platforms. Examples of players in this segment include (in alphabetical order) Atomwise, Benevolent.ai, BERG Health, Cloud Pharmaceuticals, Cyclica, Hummingbird Bioscience, InSilico Medicine, Mind the Byte, Molplex Pharmaceutical, nference, Numedii, Numerate, Standigm, twoXAR, Verge Genomics, Vium and SparkBeyond.
4. In addition, there are companies that are focused in applying deep learning in both drug discovery as well as diagnostics. Examples of such companies include (in alphabetical order) 23andMe, Appistry, Deep Genomics, Desktop Genetics, Globavir Biosciences, Google, IBM, SolveBio and Wuxi NextCODE.
5. During the last three years, heavy investments have been made in this domain. Of the overall amount invested in last 10 years (USD 1.8 billion), USD 1.6 billion was invested into deep learning initiatives in and after 2014. There are several recent examples. iCarbonX raised USD 214 million in three funding rounds (January 2016, April 2016 and July 2016), Flatiron Health received USD 175 million in Series C funding (January 2016), LAM Therapeutics witnessed funding of USD 40 million (February 2016), and Human Longevity closed a Series B funding round amounting to USD 220 million (April 2016)..
6. In the drug discovery segment, the deep learning solutions have shown to significantly reduce the cost and time spent in bringing a drug to the market. Taking a drug from discovery stage to the market is known to cost up to USD 2.5 billion and takes, on an average, close to 12 years. Deep learning models are likely to save as much as 50% of this cost and save a significant amount of time. By 2035, we have predicted annual cost savings of over USD 100 billion for the global healthcare system.
7. The adoption of deep learning models in diagnostics is also likely to provide several cost and time saving opportunities. According to our estimates, by 2035, deep learning solutions can result in annual savings of over USD 35 billion in the diagnostics segment alone. The activity is likely to be relatively more prominent in the high income countries in the near term. However, in the long term, the low radiologist to patient ratio in middle income countries is likely to provide ample growth opportunities in these countries.

RESEARCH METHODOLOGY
Most of the data presented in this report has been gathered via secondary and primary research. We have also conducted interviews with experts in the area (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Where possible, the available data has been checked for accuracy from multiple sources of information.

The secondary sources of information include
 Annual reports
 Investor presentations
 SEC filings
 Industry databases
 News releases from company websites
 Government policy documents
 Other analyst's opinion reports

While the focus has been on forecasting the market over the coming two decades, the report also provides our independent view on various non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary and primary sources of information.

CHAPTER OUTLINES
Chapter 2 provides an executive summary of the report. It offers a high level view on where the deep learning market for drug discovery and diagnostics is headed in the long term.

Chapter 3 is an introductory chapter that presents details on the digital revolution in the medical industry. It elaborates on the growth of artificial intelligence and machine learning tools, such as deep learning algorithms, along with a discussion on their potential applications in solving some of the key challenges faced by the healthcare industry. The chapter also gives an overview on the rise of big data and its role in providing personalized and evidence based care to patients.

Chapter 4 includes information on close to 100 companies that are evaluating potential applications of their proprietary deep learning solutions in the healthcare industry. The classification system used for these solutions was based on their application areas. These include drug discovery, diagnostics, clinical trial management and drug adherence programs. In addition, we have highlighted specific geographical pockets that we identified as innovation hubs in this sector.

Chapter 5 provides detailed profiles of some of the key stakeholders in this space with detailed information on their technologies, funding, collaborations and partnerships, intellectual capital, awards and recognition and activity on social media.

Chapter 6 presents a case study on two technology giants in this field, namely IBM and Google. It provides a detailed description of the initiatives being undertaken by these companies to explore the applications of deep learning in the medical field. In addition, the chapter provides a comparison of the two companies based on their respective deep learning expertise, and partnerships and acquisitions.

Chapter 7 provides information on the various investments that have been made into this industry. Our analysis revealed interesting insights on the growing interest of venture capitalists and other stakeholders in this market. In addition, we identified some of the key investors in this market.

Chapter 8 presents detailed projections related to the growth of the deep learning industry in healthcare from 2017 to 2035. To quantify the opportunity for deep learning in the drug discovery space, we have provided optimistic and conservative forecast scenarios, along with our base forecast to account for the uncertainties associated with the adoption of these technologies. The insights presented in this chapter are backed by data from close to 50 countries and highlights the opportunity for deep learning companies in diagnostics within the same regions.

Chapter 9 features a comprehensive valuation analysis of the companies that are developing deep learning solutions for applications in drug discovery and diagnostics. The chapter provides insights based on a multi-variable dependent valuation model. The model is based on the future potential of the companies’ technologies, their current popularity, funding received, year of establishment and the employed workforce in these companies.

Chapter 10 presents the opinions expressed by selected key opinion leaders on the applications and challenges associated with deep learning in the healthcare sector. The chapter provides key takeaways from presentations and videos of these experts, highlighting the future opportunity for these models within the healthcare industry.

Chapter 11 summarizes the overall report. In this chapter, we provide a recap of the key takeaways and our independent opinion based on the research and analysis described in the previous chapters.

Chapter 12 is a collection of interview transcripts of the discussions held with key stakeholders in this market. We have presented the details of our discussions with Mausumi Acharya (CEO, Advenio Technosys), Carla Leibowitz (Head of Strategy and Marketing, Arterys), and Deekshith Marla (CTO, Arya.ai).

Chapter 13 is an appendix, which provides tabulated data and numbers for all the figures in the report. In addition, the chapter includes a detailed analysis of the survey conducted with several companies to estimate the opportunity for deep learning in drug discovery and diagnostics.

Chapter 14 is an appendix, which provides the list of companies and organizations mentioned in the report.

LIST OF COMPANIES AND ORGANIZATIONS

The following companies and organizations have been mentioned in the report.
1. 23andMe
2. Accel
3. Advenio Technosys
4. Aeris Capital
5. Agfa HealthCare
6. AiCure
7. AlchemyAPI
8. Alder Hey Children’s NHS Foundation Trust
9. Alexandria Real Estate Equities
10. AlgoSurg
11. Allen & Company
12. Almaworks
13. Alphabet
14. AltaIR Capital
15. Amazon
16. AME Cloud Ventures
17. American Cancer Society
18. American Diabetes Association
19. American Sleep Apnea Association
20. American Well
21. Amplify Partners
22. Analytics Ventures
23. Anne Arundel Medical Center
24. API.AI
25. Appistry
26. Apple
27. ARCH Venture Partners
28. Arterys
29. Arya.ai
30. Asian Institute of Public Health
31. Asset Management Ventures
32. AstraZeneca
33. Atlas Ventures
34. Atomwise
35. Avalon AI
36. Baptist Health
37. Bay Labs
38. Behold.ai
39. BenevolentAI
40. BERG Health
41. BEROCEUTICA
42. Beth Israel Deaconess Medical Center
43. Bill and Melinda Gates Foundation
44. Flipkart
45. Biogen
46. Biomatics Capital
47. BioTime
48. Bloomberg Beta
49. BlueCross BlueShield Venture Partners
50. Boston Children’s Hospital
51. Brighterion
52. Butterfly Network
53. Calico Labs
54. Cambia Health Solutions
55. CAMELOT biomedical systems
56. Capital One Growth Ventures
57. Capitol Health
58. Carestream Health
59. Carnegie Mellon University
60. Casdin Capital
61. Celgene
62. Centre for Addiction and Mental Health
63. ChemDiv
64. China Bridge Capital
65. Chinese University of Hong Kong
66. Claremont Creek Ventures
67. ClearView Diagnostics
68. Cleveland Clinic
69. Cleveland Clinic Lerner College of Medicine
70. Clever Sense
71. CLI Ventures
72. Clinithink
73. Cloud Pharmaceuticals
74. Cognea
75. ContextVision
76. Convertro
77. CorTech Labs
78. CRG
79. CureMetrix
80. Cyclica
81. Cyrcadia Health
82. Dark Blue Labs
83. Data Collective Venture Capital
84. Datamind
85. Deep 6 Analytics
86. Deep Genomics
87. DeepFork Capital
88. DeepKnowledge Ventures
89. Dell
90. Desktop Genetics
91. DHHS Health Care Innovation
92. Dimagi
93. DNNresearch
94. Dolby
95. Draper Associates
96. Eastern Virginia Medical School
97. Eastside Partners
98. Eleven Two Capital
99. Emergent Medical Partners
100. Emery Capital
101. Engineering Manufacturer Entrepreneurs Resource Group
102. Enlitic
103. Enterra Solutions
104. Mayo Clinic
105. European Investment Bank
106. Eurovestech
107. Exigent Capital
108. Explorys
109. Facebook
110. Fidelity Management & Research
111. Finance Wales
112. Finnish Funding Agency for Innovation
113. Fitbit
114. First Round Capital
115. Flatiron Health
116. Formation 8
117. Foundation Capital
118. Founders Fund
119. Freenome
120. Froedtert & the Medical College of Wisconsin Cancer Network
121. Frost Data Capital
122. Gachon University Gil Medical Center
123. GE Healthcare
124. GE Ventures
125. Genentech
126. gener8tor
127. Genesis Capital Advisors
128. Gi Global Health Fund
129. Gigaom
130. Globavir Biosciences
131. Google
132. Google DeepMind
133. Google Ventures
134. Granata Decision Systems
135. Grand Challenges Canada
136. Gravity
137. Great Oaks Capital
138. GSK
139. H20.ai
140. Hangzhou Cognitive Care
141. Hanmi Science
142. Harvard Medical School
143. Harvard University
144. Healthbox
145. HelpAround
146. Hera Fund
147. Heritage Provider Network
148. Hologic
149. Howard University Hospital
150. Huazhong University of Science and Technology
151. Human Longevity
152. Hummingbird Bioscience
153. IA Ventures
154. iBinom
155. IBM
156. Icahn School of Medicine at Mount Sinai
157. iCarbonX
158. ifa systems
159. IIM Ahmedabad
160. Illumina
161. Imagia Cybernetics
162. Imaging Advantage
163. Imperial College London
164. Indian Institute of Technology Bombay
165. Indisys
166. Infermedica
167. Infosys
168. Inoveon corporation
169. InSilico Medicine
170. InSilicoScreen
171. Intel
172. Intel Capital
173. Intermountain Healthcare
174. IQ Capital Partners
175. IQbility
176. iSono Health
177. J Craig Venter Institute
178. Jetpac
179. Johnson & Johnson
180. Jvion
181. K Cube Ventures
182. Karlin Ventures
183. Keshif Ventures
184. Kheiron Medical
185. Khosla Ventures
186. King’s College London
187. Kstart
188. La Costa Investment Group
189. Laboratory Corporation of America
190. LAM Therapeutics
191. Lanza techVentures
192. LETA Capital
193. LifeExtension
194. Lightspeed Venture Partners
195. Lilly Ventures
196. London Business Angels
197. London Co-Investment Fund
198. Lumiata
199. Lunit
200. Lux Capital
201. Maccabi Healthcare Services
202. Magic Pony
203. Manipal Hospital
204. Martin Ventures
205. Massachusetts General Hospital
206. Massachusetts Institute of Technology
207. Mayo Clinic
208. MD Anderson Cancer Center
209. Medtronic
210. MedyMatch Technology
211. Merck
212. Merge Healthcare
213. Metabolon
214. Methinks Software
215. Microsoft
216. Mind the Byte
217. Mindshare Medical
218. Mitsui
219. Mohr Davidow Ventures
220. Molplex Pharmaceuticals
221. Moodstocks
222. Moorfields Eye Hospital
223. Morado Venture Partners
224. MPM Capital
225. Mumkin Hai
226. National Center for Advancing Translational Sciences
227. National Center for Research Resources
228. National Institute of Research for Tuberculosis
229. National Institute on Drug Abuse
230. National Neonatology Forum of India
231. National Science Foundation
232. Nazarbayev University
233. Nervana Systems
234. New Enterprise Associates
235. New Leaf Venture Partners
236. New York Genome Center
237. Next IT Healthcare
238. Nexus Venture Partners
239. nference
240. National Institutes of Health
241. Norwich Ventures
242. Notable Labs
243. Novartis
244. Novo Nordisk
245. Numedii
246. Numerate
247. NVIDIA
248. ODH Solutions
249. Optellum
250. Organic Research Corporation
251. OS Fund
252. Ovuline
253. Owkin
254. Park City Angel Network
255. PathAI
256. Pathway Genomics
257. Paxion Capital Partners
258. Peak Ventures
259. PerrWell
260. Personal Genome Diagnostics
261. Pfizer
262. Pharmatics
263. Phytel
264. Piraeus Jeremie Technology Catalyst Fund
265. Polaris Partners
266. Prime Health Care Services
267. Pritzker Group
268. Proteus Digital Health
269. Proximagen
270. Quest Diagnostics
271. QuikFlo Health
272. Radiology Associates of South Florida
273. Realize Ai
274. Reno Angels
275. ReviveMed Technologies
276. Rhön-Klinikum Hospitals
277. Roche
278. RSIP Vision
279. Salesforce
280. Salt Lake Life Sciences Angels
281. Samsung
282. Sandbox Industries
283. SemanticMD
284. Sentara Healthcare
285. Sentient Technologies
286. Seven Peak Ventures
287. Sheridan Healthcare
288. SickKids
289. Siemens Healthineers
290. SigTuple
291. Slow Ventures
292. SoftBank Ventures Korea
293. SolveBio
294. Southern Ontario Smart Computing Innovation Platform
295. SPARK Impact
296. SparkBeyond
297. SRI Ventures
298. SRL Diagnostics
299. Standigm
300. Stanford School of Medicine
301. Stanford University
302. StartX
303. SV Angel
304. Synthetic Genomics
305. Tailormed Technologies
306. TellApart
307. Tencent
308. Teva Pharmaceutical
309. The Indus Entrepreneurs
310. The Royal Free London NHS Foundation Trust
311. The Scripps Research Institute
312. The University of Chicago
313. Third Kind Venture Capital
314. Tianfu Group
315. Tiatros
316. Timeful
317. Tomocube
318. Topcon
319. Toth Technology
320. Transamerica
321. Tribeca Venture Partners
322. True Ventures
323. Truven Health Analytics
324. Twitter
325. Two Sigma Ventures
326. twoXAR
327. University of San Diego Medical Center
328. Under Armour
329. Universe Ventures
330. University College London Hospital NHS Trust
331. University of Calgary
332. University of California
333. University of Miami Health System
334. University of Michigan
335. University of Montreal
336. University of Oxford
337. University of Pittsburgh
338. University of Sheffield
339. University of Texas Health Science Center
340. University of the Philippines
341. University of Toronto
342. University of Vermont Health Network
343. Vanguard Atlantic
344. Vcanbio Cell & Gene Engineering Corporation
345. VentureNursery
346. Verge Genomics
347. VisExcell
348. Vision Factory
349. Vision Genomics
350. Vium
351. Viv
352. vRad (Virtual Radiologic)
353. VUNO
354. WellPoint
355. Wildcard Pharmaceutical Consulting
356. Wilmington Pharmatech
357. Woodford Investment Management
358. WuXi Healthcare Ventures
359. Wuxi NextCODE
360. Xfund
361. Y Combinator
362. YourNest Angel Fund
363. Zebra Medical Vision
364. Zhongyuan Union
365. Zone Startups India


1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Chapter Outlines
2. EXECUTIVE SUMMARY
3. INTRODUCTION
3.1. Humans, Machines and Intelligence
3.2. Artificial Intelligence
3.3. The Science of Learning
3.3.1. Teaching Machines
3.3.1.1. Machines for Computing
3.3.1.2. Understanding Human Brain: Way to Artificial Intelligence
3.4. The Big Data Revolution
3.4.1. Big Data: An Introduction
3.4.2. Big Data: Internet of Things (IoT)
3.4.3. Big Data: A Growing Trend
3.4.4. Big Data: Application Areas
3.4.4.1. Big Data Analytics in Healthcare: Collaborating For Value
3.4.4.2. Machine Learning
3.4.4.3. Deep Learning: The Amalgamation of Machine Learning and Big Data
3.5. Deep Learning in Healthcare
3.5.1. Personalized Medicine
3.5.2. Lifestyle Management
3.5.3. Wearable Devices
3.5.4. Drug Discovery
3.5.5. Clinical Trial Management
3.5.6. Diagnostics
4. MARKET OVERVIEW
4.1. Chapter Overview
4.2. Deep Learning in Drug Discovery and Diagnostics: Market Landscape
4.2.1. Deep Learning in Drug Discovery and Diagnostics: Distribution by Specialization
4.2.2. Deep Learning in Drug Discovery and Diagnostics: Distribution by Geographical Location
4.2.3. Deep Learning in Drug Discovery and Diagnostics: Distribution by Year of Establishment
4.3. Deep Learning in Drug Discovery
4.3.1. Deep Learning in Drug Discovery: Distribution by Type of Solution
4.3.2. Deep Learning in Drug Discovery: Distribution by Area of Focus
4.3.3. Deep Learning in Drug Discovery: Distribution by Therapeutic Area
4.3.4. Deep Learning in Drug Discovery: Regional Mapping
4.4. Deep Learning in Diagnostics
4.4.1. Deep Learning in Diagnostics: Distribution by Type of Solution
4.4.2. Deep Learning in Diagnostics: Distribution by Type of Input Data
4.4.3. Deep Learning in Diagnostics: Distribution by Therapeutic Area
4.4.4. Deep Learning in Diagnostics: Regional Mapping
4.5. Deep Learning in Drug Discovery and Diagnostics
4.5.1. Deep Learning in Drug Discovery and Diagnostics: Regional Mapping
4.6. Deep Learning in Drug Discovery and Diagnostics: Non-Industry Players
5. COMPANY PROFILES
5.1. Chapter Overview
5.2. Advenio Technosys
5.2.1. Company Overview
5.2.2. Technology and Services
5.2.3. Venture Funding
5.2.4. Intellectual Capital
5.2.5. Awards and Achievements
5.2.6. Social Media Activity
5.3. AiCure
5.3.1. Company Overview
5.3.2. Technology and Services
5.3.3. Venture Funding
5.3.4. Intellectual Capital
5.3.5. Awards and Achievements
5.3.6. Social Media Activity
5.4. Atomwise
5.4.1. Company Overview
5.4.2. Technology and Services
5.4.3. Venture Funding
5.4.4. Intellectual Capital
5.4.5. Social Media Analysis
5.5. BenevolentAI
5.5.1. Company Overview
5.5.2. Technology and Services
5.5.3. Venture Funding
5.5.4. Social Media Activity
5.6. Butterfly Network
5.6.1. Company Overview
5.6.2. Technology and Services
5.6.3. Venture Funding
5.6.4. Intellectual Capital
5.6.5. Awards and Achievements
5.6.6. Social Media Activity
5.7. Enlitic
5.7.1. Company Overview
5.7.2. Technology and Services
5.7.3. Venture Funding
5.7.4. Intellectual Property
5.7.5. Awards and Achievements
5.7.6. Social Media Activity
5.8. Human Longevity
5.8.1. Company Overview
5.8.2. Technology and Services
5.8.3. Venture Funding
5.8.4. Intellectual Capital
5.8.5. Awards and Achievements
5.8.6. Social Media Activity
5.9. InSilico Medicine
5.9.1. Company Overview
5.9.2. Technology and Services
5.9.3. Venture Funding
5.9.4. Intellectual Capital
5.9.5. Awards and Achievements
5.9.6. Social Media Activity
5.10. twoXAR
5.10.1. Company Overview
5.10.2. Technology and Services
5.10.3. Venture Funding
5.10.4. Intellectual Capital
5.10.5. Social Media Activity
5.11. Zebra Medical Vision
5.11.1. Company Overview
5.11.2. Technology and Services
5.11.3. Venture Funding
5.11.4. Intellectual Capital
5.11.5. Social Media Activity
6. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND
6.1. Chapter Overview
6.2. IBM
6.2.1. Company Overview
6.2.2. Financial Information
6.2.3. IBM Watson
6.3. Google
6.3.1. Company Overview
6.3.2. Financial Information
6.3.3. Google DeepMind
6.4. IBM v/s Google: Artificial Intelligence Acquisitions Portfolio
6.5. IBM v/s Google: Healthcare Partnerships and Collaborations
6.6. IBM v/s Google: Future Outlook and Primary Concerns
7. CAPITAL INVESTMENTS AND FUNDING
7.1. Chapter Overview
7.2. Deep Learning Market: Funding Instances
7.2.1. Funding Instances: Distribution by Year
7.2.2. Funding Instances: Distribution by Type of Funding
7.2.3. Leading Deep Learning Companies: Evaluation by Number of Funding Instances
7.2.4. Leading VC Firms / Investors: Evaluation by Number of Funding Instances
8. OPPORTUNITY ANALYSIS
8.1. Chapter Overview
8.2. Opportunity for Deep Learning in Drug Discovery
8.2.1. Forecast Methodology
8.2.2. Key Assumptions
8.2.3. Overall Deep Learning Market in Drug Discovery, 2017-2035
8.2.4. Comparative Summary
8.3. Opportunity for Deep Learning in Diagnostics
8.3.1. Forecast Methodology
8.3.2. Key Assumptions
8.3.3. Overall Deep Learning Market in Diagnostics, 2017-2035
8.4. Overall Deep Learning Market in Drug Discovery and Diagnostics, 2017-2035
9. COMPANY VALUATION ANALYSIS
9.1. Chapter Overview
9.2. Company Valuation: Methodology
9.3. Company Valuation: Categorization by Multiple Parameters
9.3.1. Categorization by Twitter Score
9.3.2. Categorization by Followers Score
9.3.3. Categorization by Google Hits Score
9.3.4. Categorization by Uniqueness Score
9.3.5. Categorization by Website Score
9.3.6. Categorization by Awards Score
9.3.7. Categorization by Weighted Average Score
9.3.8. Company Valuation: Roots Analysis Proprietary Scores
10. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
10.1. Chapter Overview
10.2. Industry Experts
10.2.1. Alex Jaimes, CTO, AiCure
10.2.2. Jeremy Howard, Founder, Enlitic
10.2.3. Riley Doyle, CEO, Desktop Genomics
10.3. University and Hospital Experts
10.3.1. Dr. Steven Alberts, Chairman of Medical Oncology, Mayo Clinic
10.3.2. Neil Lawrence, Professor, University of Sheffield
10.3.3. Yoshua Bengio, Professor, Université de Montréal
10.4. Venture Capital Experts
10.4.1. Robert Perl, CEO, Permanente Medical Group; Vinod Khosla, CEO, Khosla Ventures; Abraham Verghese, Professor, Stanford School of Medicine
10.5. Other Expert Opinions
11. CONCLUSION
11.1. Big Data and Deep Learning are Touted as the Next Big Thing in Digital Healthcare
11.2. The Field is Witnessing Rising Interest from Technology and Pharmaceutical Giants
11.3. Drug Discovery and Diagnostics have Emerged as the Major Application Areas for Deep Learning in Healthcare
11.4. Start-ups, Backed by Venture Capital Investors, are Driving Innovation in the Market
11.5. The Applications of Deep Learning are Expected to Result in Significant Time and Cost Savings
11.6. Data Sharing and Security Pose the Biggest Hurdles to the Implementation of Deep Learning Solutions
11.7. Certain Regulatory and Socio-Economic Concerns have Emerged as Additional Roadblocks in this Domain
12. INTERVIEW TRANSCRIPTS
12.1. Mausumi Acharya, CEO, Advenio Technosys
12.2. Carla Leibowitz, Head of Strategy and Marketing, Arterys
12.3. Deekshith Marla, CTO, Arya.ai and Sanjay Bhadra, COO, Arya.ai
13. APPENDIX 1: TABULATED DATA
14. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS
LIST OF FIGURES
Figure 3.1 Observational Learning: Key Stages of Learning
Figure 3.2 Understanding Neurons and the Human Brain: Key Scientific Contributors
Figure 3.3 Big Data: The Three V’s
Figure 3.4 Internet of Things: Illustrative Framework
Figure 3.5 Internet of Things: Applications in Healthcare
Figure 3.6 Big Data: Google Trends
Figure 3.7 Big Data: Application Areas
Figure 3.8 Big Data: Opportunities in Healthcare
Figure 3.9 Machine Learning Algorithm: Workflow
Figure 3.10 Machine Learning Algorithms: Timeline
Figure 3.11 Neural Networks: Architecture
Figure 3.12 Deep Learning: Image Recognition
Figure 3.13 Google Trends: Artificial Intelligence v/s Machine Learning v/s Deep Learning v/s Cognitive Computing
Figure 3.14 Google Trends: Popular Keywords (Deep Learning)
Figure 3.15 Deep Learning Frameworks: Relative Performance
Figure 3.16 Personalized Medicine: Applications in Healthcare
Figure 4.1 Deep Learning in Drug Discovery and Diagnostics: Distribution by Specialization and Type
Figure 4.2 Deep Learning in Drug Discovery and Diagnostics: Distribution by Geographical Location and Area of Specialization
Figure 4.3 Deep Learning in Drug Discovery and Diagnostics: Distribution by Founding Year and Specialization
Figure 4.4 Deep Learning in Drug Discovery: Distribution by Type of Solution
Figure 4.5 Deep Learning in Drug Discovery: Distribution by Focus Area
Figure 4.6 Deep Learning in Drug Discovery: Distribution by Therapeutic Area
Figure 4.7 Deep Learning in Drug Discovery: Regional Mapping
Figure 4.8 Deep Learning in Diagnostics: Distribution by Type of Solution
Figure 4.9 Deep Learning in Diagnostics: Distribution by Type of Input Data
Figure 4.10 Deep Learning in Diagnostics: Distribution of Service Providers by Key Modifications
Figure 4.11 Deep Learning in Diagnostics: Distribution by Therapeutic Area
Figure 4.12 Deep Learning in Diagnostics: Regional Mapping
Figure 4.13 Deep Learning in Drug Discovery and Diagnostics: Geographical Distribution
Figure 4.14 Deep Learning in Drug Discovery and Diagnostics, Non-Industrial Players: Regional Mapping
Figure 5.1 Advenio Technosys: Company Overview
Figure 5.2 Advenio Technosys: Social Media Analysis
Figure 5.3 AiCure: Company Overview
Figure 5.4 AiCure: Social Media Analysis
Figure 5.5 Atomwise: Company Overview
Figure 5.6 BenevolentAI: Company Overview
Figure 5.7 BenevolentAI: Social Media Analysis
Figure 5.8 Butterfly Network: Company Overview
Figure 5.9 Butterfly Network: Social Media Analysis
Figure 5.10 Enlitic: Company Overview
Figure 5.11 Enlitic: Social Media Analysis
Figure 5.12 Human Longevity: Company Overview
Figure 5.13 Human Longevity: Social Media Analysis
Figure 5.14 InSilico Medicine: Company Overview
Figure 5.15 InSilico Medicine: Social Media Analysis
Figure 5.16 twoXAR: Company Overview
Figure 5.17 twoXAR: Social Media Analysis
Figure 5.18 Zebra Medical Vision: Company Overview
Figure 5.19 Zebra Medical Vision: Social Media Analysis
Figure 6.1 IBM: Annual Revenues, 2011-Q3 2016 (USD Billion)
Figure 6.2 Alphabet: Annual Revenues, 2011-Q3 2016 (USD Billion)
Figure 6.3 IBM versus Google: Acquisition Trend (Artificial Intelligence), 2011-2016
Figure 7.1 Funding Instances: Distribution by Year, 2007-2016
Figure 7.2 Funding Instances: Amount Invested Per Year (USD Million), 2007-2016
Figure 7.3 Funding Instances: Distribution by Type of Funding, 2007-2016
Figure 7.4 Funding Instances: Distribution by Total Amount Invested in Each Category, 2007-2016 (USD Million)
Figure 7.5 Leading Companies: Evaluation by Number of Funding Instances
Figure 7.6 Leading VC Firms: Evaluation by Number of Instances
Figure 8.1 Drug Approval: Historical Data, 2005-2015
Figure 8.2 Opportunity for Deep Learning in Drug Discovery: Future Market Scenarios
Figure 8.3 Deep Learning Market in Drug Discovery, Short-Midterm (2017-2026): Base Scenario (USD Billion)
Figure 8.4 Deep Learning Market in Drug Discovery, Long Term (2026-2035): Base Scenario (USD Billion)
Figure 8.5 Deep Learning Market in Drug Discovery (2017-2035): Market Scenarios (USD Billion)
Figure 8.6 Deep Learning in Diagnostics: Distribution of Radiologists (per 100,000 population), High Income Countries
Figure 8.7 Deep Learning in Diagnostics: Distribution of Radiologists (per 100,000 population), Middle Income Countries
Figure 8.8 Deep Learning in Diagnostics: Global Distribution of Radiology Images
Figure 8.9 Deep Learning in Diagnostics: Deep Learning Efficiency Profile
Figure 8.10 Deep Learning Market in Diagnostics, Short-Midterm (2017-2026) (USD Billion)
Figure 8.11 Deep Learning Market in Diagnostics, Long Term (2026-2035): Base Scenario (USD Billion)
Figure 8.12 Deep Learning Market in Diagnostics: Market Distribution
Figure 8.13 Overall Deep Learning Market in Drug Discovery and Diagnostics, (2017-2035): Base Scenario (USD Billion)
Figure 9.1 Company Valuation Analysis: A/F Ratio, Input Dataset
Figure 9.2 Company Valuation Analysis: A/Y Ratio, Input Dataset
Figure 9.3 Company Valuation Analysis: A/E Ratio, Input Dataset
Figure 9.4 Company Valuation Analysis: Categorization by Tweets Score
Figure 9.5 Company Valuation Analysis: Categorization by Followers Score
Figure 9.6 Company Valuation Analysis: Categorization by Google Hits Score
Figure 9.7 Company Valuation Analysis: Categorization by Uniqueness Score
Figure 9.8 Company Valuation Analysis: Categorization by Website Score
Figure 9.9 Company Valuation Analysis: Categorization by Awards Score
Figure 9.10 Company Valuation Analysis: Categorization by Weighted Average Score
Figure 9.11 Company Valuation Analysis: Unicorns in Deep Learning
Figure 11.1 Deep Learning Market in Drug Discovery and Diagnostics, (2017-2035): Base Scenario (USD Billion)
LIST OF TABLES
Table 3.1 Machine Learning: A Brief History
Table 4.1 Drug Discovery and Diagnostics: Deep Learning Service Providers
Table 4.2 Deep Learning Industry Players: Drug Discovery
Table 4.3 Deep Learning Industry Players: Diagnostics
Table 4.4 Deep Learning Industry Players: Drug Discovery and Diagnostics
Table 4.5 Deep Learning Non-Industry Players: Drug Discovery and Diagnostics
Table 5.1 Advenio Technosys: Venture Capital Funding
Table 5.2 Advenio Technosys: Patent Portfolio
Table 5.3 AiCure: Venture Capital Funding
Table 5.4 AiCure: Patent Portfolio
Table 5.5 Atomwise: Key Partnerships
Table 5.6 Atomwise: Venture Capital Funding
Table 5.7 Atomwise: Patent Portfolio
Table 5.8 BenevolentAI: Venture Capital Funding
Table 5.9 Butterfly Network: Venture Capital Funding
Table 5.10 Butterfly Network: Patent Portfolio
Table 5.11 Enlitic: Venture Capital Funding
Table 5.12 Human Longevity: Partnerships and Collaborations
Table 5.13 Human Longevity: Venture Capital Funding
Table 5.14 Human Longevity: Patent Portfolio
Table 5.15 InSilico Medicine: Partnerships and Collaborations
Table 5.16 InSilico Medicine: Venture Capital Funding
Table 5.17 InSilico Medicine: Patent Portfolio
Table 5.18 twoXAR: Partnerships and Collaborations
Table 5.19 twoXAR: Venture Capital Funding
Table 5.20 Zebra Medical Vision: Partnerships and Collaborations
Table 5.21 Zebra Medical Vision: Venture Capital Funding
Table 5.22 Zebra Medical Vision: Patent Portfolio
Table 6.1 IBM: Artificial Intelligence Acquisitions
Table 6.2 Google: Artificial Intelligence Acquisitions
Table 6.3 IBM Watson: Collaborations & Partnerships in Healthcare
Table 6.4 Google DeepMind: Collaborations & Partnerships in Healthcare
Table 7.1 List of Funding Instances and Investors Involved
Table 7.2 Deep Learning in Drug Discovery & Diagnostics Market: Types of Funding, 2007- 2016
Table 8.1 Opportunity for Deep Learning in Drug Discovery: Survey Responses
Table 8.2 Opportunity for Deep Learning in Drug Discovery: Forecast Parameters
Table 8.3 Deep Learning in Drug Discovery: Conservative Scenario, Key Parameters
Table 8.4 Deep Learning in Drug Discovery: Base Scenario Parameters
Table 8.5 Deep Learning in Drug Discovery: Optimistic Scenario Parameters
Table 9.1 Company Valuation Analysis: Sample Dataset
Table 9.2 Company Valuation Analysis: Weighted Average Evaluation
Table 9.3 Company Valuation Analysis: Estimated Valuation
Table 9.4 Company Valuation Analysis: Distribution by Specialization
Table 13.1 Deep Learning in Drug Discovery and Diagnostics: Distribution by Specialization
Table 13.2 Deep Learning in Drug Discovery and Diagnostics: Distribution by Service Provider Type
Table 13.3 Deep Learning in Drug Discovery and Diagnostics: Distribution by Geographical Location and Area of Specialization
Table 13.4 Deep Learning in Drug Discovery and Diagnostics: Distribution by Founding Year and Specialization
Table 13.5 Deep Learning in Drug Discovery: Distribution by Type of Solution
Table 13.6 Deep Learning in Drug Discovery: Distribution by Focus Area
Table 13.7 Deep Learning in Drug Discovery: Regional Mapping
Table 13.8 Deep Learning in Diagnostics: Distribution by Type of Solution
Table 13.9 Deep Learning in Diagnostics: Distribution by Type of Input Data
Table 13.10 Deep Learning in Diagnostics: Regional Mapping
Table 13.11 Deep Learning in Drug Discovery and Diagnostics: Regional Mapping
Table 13.12 Deep Learning in Drug Discovery and Diagnostics, Non-Industrial Players: Geographical Distribution
Table 13.13 IBM: Annual Revenues, 2011-Q3 2016 (USD Billion)
Table 13.14 Alphabet: Annual Revenues, 2011-Q3 2016 (USD Billion)
Table 13.15 IBM versus Google: Acquisition Trend (Artificial Intelligence), 2011-2016
Table 13.16 Funding Instances: Distribution by Year, 2007-2016
Table 13.17 Funding Instances: Distribution by Type of Funding, 2007-2016
Table 13.18 Funding Instances: Distribution by Total Amount Invested in Each Category, 2007-2016 (USD Million)
Table 13.19 Leading Companies: Evaluation by Number of Funding Instances
Table 13.20 Leading Companies: Evaluation by Number of Funding Instances
Table 13.21 Drug Approval: Historical Data, 2005-2015
Table 13.22 Deep Learning Market in Drug Discovery, Short-Midterm (2017-2026): Base Scenario (USD Billion)
Table 13.23 Deep Learning Market in Drug Discovery, Long Term (2026-2035): Base Scenario (USD Billion)
Table 13.24 Deep Learning Market in Drug Discovery, Short-Midterm (2017-2026): Optimistic Scenario (USD Billion)
Table 13.25 Deep Learning Market in Drug Discovery, Long Term (2026-2035): Optimistic Scenario (USD Billion)
Table 13.26 Deep Learning Market in Drug Discovery, Short-Midterm (2017-2026): Conservative Scenario (USD Billion)
Table 13.27 Deep Learning Market in Drug Discovery, Long Term (2026-2035): Conservative Scenario (USD Billion)
Table 13.29 Deep Learning Market in Drug Discovery (2017-2035): Market Scenarios (USD Billion)
Table 13.29 Deep Learning in Diagnostics: Distribution of Radiologists (per 100,000 population), High Income Countries
Table 13.30 Deep Learning in Diagnostics: Distribution of Radiologists (per 100,000 population), Middle Income Countries
Table 13.31 Deep Learning in Diagnostics: Deep Learning Efficiency Profile
Table 13.32 Deep Learning Market in Diagnostics, Short-Midterm (2017-2026) (USD Billion)
Table 13.33 Deep Learning Market in Diagnostics, Long Term (2026-2035): Base Scenario (USD Billion)
Table 13.34 Deep Learning Market in Diagnostics: Market Distribution (USD Billion)
Table 13.35 Deep Learning Market in Diagnostics: Market Distribution (USD Billion)
Table 13.36 Company Valuation Analysis: Valuation Ratios, Input Dataset

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