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Deep Learning in Drug Discovery and Diagnostics, 2017 - 2035

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  • Published
    February 2017

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Example Insights

  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.

Overview

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).

Contents

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).

Chapter13  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.

Table of Contents

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, Universit de Montral
 
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 13.28  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

Listed Companies

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

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