Deep Learning in Drug Discovery market 2022 – Industry Growth by 2035
Roots Analysis has done a detailed study on Deep Learning in Drug Discovery and Deep Learning in Diagnostics Market, covering important aspects of the industry and identifying key future growth opportunities.
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Roots Analysis has done a detailed study on Deep Learning in Drug Discovery and Deep Learning in Diagnostics Market, covering important aspects of the industry and identifying key future growth opportunities.
Key Market Insights
§ Presently, more than 70 players across the globe claim to offer deep learning technologies for potential applications across various steps of drug discovery and development process
§ Majority (70%) of the stakeholders employ proprietary deep learning-based technologies in drug discovery to offer big data analysis
§ Nearly 50% of the deep learning-based diagnostic providers are based in North America; most such players offer technologies for use across medical imaging and medical diagnosis related applications
§ Around 70% of the players engaged in offering deep learning solutions for diagnostics have been established post-2011; majority of the players offer solutions focused on oncological disorders
§ Foreseeing the lucrative potential, a large number of players have made investments worth over USD 15 billion, across 210 funding instances, to advance the initiatives undertaken by industry stakeholders
§ Over the past few years, more than 704,000 patients have been recruited / enrolled in clinical trials registered for deep learning-based solutions / diagnostics across different geographies
§ Our proprietary benchmarking analysis, based on a variety of parameters, indicates the leading start-ups / small firms that are spearheading innovation in this domain
§ Some players have managed to establish strong competitive positions; in the near future, we expect multiple acquisitions to take place wherein the relative valuation of a firm is likely to be a key determinant
§ Increasing adoption of deep learning technologies in the life sciences and healthcare industry is anticipated to create profitable business opportunities for the technology developers
§ The market opportunity associated with deep learning in drug discovery is expected to witness an annualized growth rate of 23% over the coming 12 years
§ In the long term, the opportunity for deep learning in diagnostics is projected to grow exponentially; the market is likely to be well distributed across various therapeutic areas and geographical regions
Table of Contents
1. PREFACE
1.1. Introduction
1.2. Key Market Insights
1.3. Scope of the Report
1.4. Research Methodology
1.5. Frequently Asked Questions
1.6. Chapter Outlines
2. EXECUTIVE SUMMARY
3. INTRODUCTION
3.1. Humans, Machines and Intelligence
3.2. The Science of Learning
3.2.1. Teaching Machines
3.2.1.1. Machines for Computing
3.2.1.2. Artificial Intelligence
3.3. The Big Data Revolution
3.3.1. Overview of Big Data
3.3.2. Role of Internet of Things (IoT)
3.3.3. Key Application Areas of Big Data
3.3.3.1. Big Data Analytics in Healthcare
3.3.3.2. Machine Learning
3.3.3.3. Deep Learning
3.4. Deep Learning in Healthcare
3.4.1. Personalized Medicine
3.4.2. Lifestyle Management
3.4.3. Drug Discovery
3.4.4. Clinical Trial Management
3.4.5. Diagnostics
3.5. Concluding Remarks
4. MARKET OVERVIEW: DEEP LEARNING IN DRUG DISCOVERY
4.1. Chapter Overview
4.2. Deep Learning in Drug Discovery: Overall Market Landscape of Service / Technology Providers
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters
4.2.4. Analysis by Application Area
4.2.5. Analysis by Focus Area
4.2.6. Analysis by Therapeutic Area
4.2.7. Analysis by Operational Model
4.2.7.1. Analysis by Service Centric Model
4.2.7.2. Analysis by Product Centric Model
5. MARKET OVERVIEW: DEEP LEARNING IN DIAGNOSTICS
5.1. Chapter Overview
5.2. Deep Learning in Diagnostics: Overall Market Landscape of Service / Technology Providers
5.2.1. Analysis by Year of Establishment
5.2.2. Analysis by Company Size
5.2.3. Analysis by Location of Headquarters
5.2.4. Analysis by Application Area
5.2.5. Analysis by Focus Area
5.2.6. Analysis by Therapeutic Area
5.2.7. Analysis by Type of Offering / Solution
5.2.8. Analysis by Compatible Device
6. COMPANY PROFILES
6.2. Aegicare
6.2.1. Company Overview
6.2.2. Service Portfolio
6.2.3. Recent Developments and Future Outlook
6.3. Aiforia Technologies
6.4. Ardigen
6.5. Berg
6.6. Google
6.7. Huawei
6.8. Merative
6.9. Nference
6.10. Nvidia
6.11. Owkin
6.12. Phenomic AI
6.13. Pixel AI
7. PORTER’S FIVE FORCES ANALYSIS
7.1. Chapter Overview
7.2. Methodology and Assumptions
7.3. Key Parameters
7.3.1. Threats of New Entrants
7.3.2. Bargaining Power of Companies Using Deep Learning for Drug Discovery and Diagnostics
7.3.3. Bargaining Power of Drug Developers
7.3.4. Threats of Substitute Technologies
7.3.5. Rivalry Among Existing Competitors
7.4. Concluding Remarks
8. CLINICAL TRIAL ANALYSIS
8.1. Chapter Overview
8.2. Scope and Methodology
8.3 Deep Learning Market: Clinical Trial Analysis
8.3.1. Analysis by Trial Registration Year
8.3.2. Analysis by Trial Status
8.3.3. Analysis by Trial Registration Year and Patient Enrollment
8.3.4. Analysis by Trial Registration Year and Trial Status
8.3.5. Analysis by Type of Sponsor / Collaborator
8.3.6. Analysis by Therapeutic Area
8.3.7. Word Cloud: Trial Focus Area
8.3.8. Analysis by Study Design
8.3.9. Geographical Analysis by Number of Clinical Trials
8.3.10. Geographical Analysis by Trial Registration Year and Patient Population
8.3.11. Leading Organizations: Analysis by Number of Registered Trials
9. FUNDING AND INVESTMENT ANALYSIS
9.1. Chapter Overview
9.2. Types of Funding
9.3. Deep Learning Market: Funding and Investment Analysis
9.3.1. Analysis by Year of Funding
9.3.2. Analysis by Amount Invested
9.3.3. Analysis by Type of Funding
9.3.4. Analysis by Year and Type of Funding
9.3.5. Analysis by Focus Areas
9.3.6. Analysis by Therapeutic Area
9.3.7. Analysis by Geography
9.3.8. Most Active Players: Analysis by Number of Funding Instances
9.3.9. Most Active Players: Analysis by Amount Invested
9.3.10. Most Active Investors: Analysis by Number of Funding Instances
10. START-UP HEALTH INDEXING
10.1. Chapter Overview
10.2. Start-ups Focused on Deep Learning in Drug Discovery
10.2.1. Methodology and Key Parameters
10.2.2. Analysis by Location of Headquarters
10.3. Benchmarking Analysis of Start-ups Focused on Deep Learning in Drug Discovery
10.3.1. Analysis by Focus Area
10.3.2. Analysis by Therapeutic Area
10.3.3. Analysis by Operational Model
10.3.4. Start-up Health Indexing: Roots Analysis Perspective
10.4. Start-ups Focused on Deep Learning in Diagnostics
10.4.1. Methodology and Key Parameters
10.4.2. Analysis by Location of Headquarters
10.5. Benchmarking Analysis of Start-ups Focused on Deep Learning in Diagnostics
10.5.1. Analysis by Focus Area
10.5.2. Analysis by Therapeutic Area
10.5.3. Analysis by Compatible Device
10.5.4. Analysis by Type of Offering
10.5.5. Start-up Health Indexing: Roots Analysis Perspective
11. COMPANY VALUATION ANALYSIS
11.1. Chapter Overview
11.2. Company Valuation Analysis: Key Parameters
11.3. Methodology
11.4. Company Valuation Analysis: Roots Analysis Proprietary Scores
12. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DRUG DISCOVERY
12.1. Chapter Overview
12.2. Key Assumptions and Methodology
12.3. Overall Deep Learning in Drug Discovery Market, 2023-2035
12.3.1. Deep Learning in Drug Discovery Market: Analysis by Therapeutic Area, 2023-2035
12.3.2. Deep Learning in Drug Discovery Market: Analysis by Geography, 2023-2035
12.3.3. Deep Learning in Drug Discovery: Cost Saving Analysis
13. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DIAGNOSTICS
13.1. Chapter Overview
13.2. Key Assumptions and Methodology
13.3. Overall Deep Learning in Diagnostics Market, 2023-2035
13.3.1. Deep Learning in Diagnostics Market: Analysis by Therapeutic Area, 2023-2035
13.3.2. Deep Learning in Diagnostics Market: Analysis by Geography, 2023-2035
14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
14.1. Chapter Overview
14.2. Sean Lane, Chief Executive Officer (Olive)
14.3. Junaid Kalia, Founder (NeuroCare.AI) and Adeel Memon, Assistant Professor, Neurology Specialist (West Virginia University Hospitals)
14.4. David Reich, President / Chief Operating Officer (The Mount Sinai Hospital) and Robbie Freeman, Vice President of Clinical Innovation (The Mount Sinai Hospital)
14.5. Elad Benjamin, Vice President, Business Leader Clinical Data Services (Philips) and Jonathan Laserson, Senior Deep Learning Researcher (Apple)
14.6. Kevin Lyman, Founder and Chief Science Officer (Enlitic)
15. CONCLUDING REMARKS
16. INTERVIEW TRANSCRIPTS
16.1. Chapter Overview
16.2. Nucleai
16.2.2. Interview Transcript: Avi Veidman, Chief Executive Officer, Yoav Blum, Director of AI and Ken Bloom, Head of Pathology
16.3. Mediwhale
16.3.2. Interview Transcript: Kevin Choi, Chief Executive Officer
16.4. Arterys
16.4.2. Interview Transcript: Babak Rasolzadeh, Former Vice President of Product and Software Development
16.5. AlgoSurg
16.5.2. Interview Transcript: Vikas Karade, Founder, Chief Executive Officer
16.6. ContextVision
16.6.2. Interview Transcript: Walter de Back, Former Research Scientist
16.7. Advenio Technosys
16.7.2. Interview Transcript: Mausumi Acharya, Chief Executive Officer
16.8. Arterys
16.8.2. Interview Transcript: Carla Leibowitz, Head of Strategy and Marketing
16.9. Arya.ai
16.9.2. Interview Transcript: Deekshith Marla, Founder and Chief Technology Officer and Sanjay Bhadra, Chief Business Officer
17. APPENDIX 1: TABULATED DATA
18. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS
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